Emotion processing systems and methods

ABSTRACT

A system for conducting parallelization of tasks is disclosed. The system includes an interface for receiving messages comprising a representation of logic describing two tasks to be executed in parallel, the message further comprising a content payload for use in the tasks. The system further includes a parallel processing grid comprising devices running on independent machines, each device comprising a processing manager unit and at least two processing units. The processing manager is configured to parse the received messages and to distribute the at least two tasks to the at least two processing units for independent and parallel processing relative to the content payload.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application claims the benefit of and priority to U.S. ProvisionalPatent Application No. 61/908,031 filed Nov. 22, 2013, U.S. ProvisionalPatent Application No. 61/925,178 filed Jan. 8, 2014, U.S. ProvisionalPatent Application No. 61/968,334 filed Mar. 20, 2014, and U.S.Provisional Patent Application No. 61/994,117 filed May 15, 2014, eachof which is incorporated by reference herein in its entirety.

BACKGROUND

Big data can be defined as any data that is too large, too complete,and/or too expensive to process using existing technologies andarchitectures.

Conventional parallel processing approaches have utilized a threadedarchitecture in an attempt to achieve processing power scaling. However,this approach has only proven to be somewhat useful, because threadedarchitectures often share all resources such as memory, I/O, diskresources, CPU resources, and other system resources. Given thissharing, threads need to be carefully managed. This management oftenmeans that “parallel” threads are not truly asynchronous orindependently parallel. Left unmanaged, a shared-but-threadedarchitecture can result in a competition for resources between threads.This competition can result in issues such as thread locking, racing,and blocking, among other issues. Even in instances with adequate CPUbandwidth, these issues can cause bottlenecks, artificial delays, and/orthe overall sub-optimization of resources.

It is challenging and difficult to design systems for processing in aparallel fashion and a high degree of flexibility.

SUMMARY

One embodiment of the present disclosure relates to a system forconducting parallelization of tasks. The system includes an interfacefor receiving messages comprising a representation of logic describingtwo tasks to be executed in parallel, the message further comprising acontent payload for use in the tasks. The system further includes aparallel processing grid comprising devices running on independentmachines, each device comprising a processing manager unit and at leasttwo processing units. The processing manager is configured to parse thereceived messages and to distribute the at least two tasks to the atleast two processing units for independent and parallel processingrelative to the content payload.

Another embodiment of the present disclosure relates to a computerizedmethod for processing tasks. The method includes receiving a messagedescribing at least two processing tasks to be parallelized andcompleted relative to a payload content of the message. The method alsoincludes parsing the received message to identify the at least two tasksfor parallelization. The method further includes distributing the tasks,in parallel, to a discrete processing unit. The method also includes ateach discrete processing unit, completing the entirety of its taskasynchronously with another discrete processing unit.

The messaging source can have a website crawler. The website crawler maybe configured to generate the message having the representation of logicdescribing two tasks to be executed in parallel and the content payloadfor use in the tasks. The messaging source can have a streaming datainterface for receiving streaming data. The messaging source may beconfigured to process the streaming data and to generate the messageusing the streaming data. The messaging source is configured to use thestreaming data to create the payload and wherein the identification ofthe tasks are not a part of the original streaming data. The messagingsource may include a query engine for querying a data source and forgenerating a series of the messages using the query results. In someexemplary embodiments, the identification of the tasks are not a part ofthe query result data. The interface may include a framework managerconfigured to queue the messages. Each processing manager unit may beconfigured to request new messages from the queue when resources permit.The messages utilize a mark-up language to identify the tasks to becompleted in parallel and the content payload.

BRIEF DESCRIPTION OF THE FIGURES

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features,aspects, and advantages of the disclosure will become apparent from thedescription, the drawings, and the claims.

FIG. 1 is a system for conducting parallelization of tasks, according toan exemplary embodiment.

FIG. 2 is a simplified block diagram of the parallel grid of FIG. 1,according to an exemplary embodiment.

FIG. 3 is a flow chart of a process for parallelizing tasks utilizingthe system described in FIGS. 1 and 2, according to an exemplaryembodiment.

FIG. 4 is an example of the source code for the HTML_CLEANER plug-in(e.g., as referenced in FIG. 2), according to an exemplary embodiment.

FIG. 5 is another example message for commanding task parallelization,according to an exemplary embodiment.

FIG. 6A is a block diagram of a parallel computing device that can beused in the parallel processing grid of the previous Figures, accordingto an exemplary embodiment.

FIG. 6B is a block diagram of a server device including a frameworkmanager that can be used with the parallel computing device andprocessing grid of the previous Figures, according to an exemplaryembodiment.

FIG. 7A is a process for processing message payload content to determineemotional scores and behavioral correlates, according to an exemplaryembodiment.

FIG. 7B is a flowchart illustrating the process of model creation andvalidation according to an exemplary embodiment.

FIG. 8A illustrates, according to one embodiment, a user interface pageshowing information regarding attributes of one or more brands.

FIG. 8B illustrates, according to one embodiment, a user interface pageshowing information regarding sentiment of one or more brands.

FIG. 8C illustrates, according to one embodiment, a user interface pageshowing information regarding a comparison of one or more brands.

FIG. 8D illustrates, according to one embodiment, a user interface pageshowing information regarding brand health of one or more brands.

FIG. 8E illustrates, according to one embodiment, a user interface pageshowing information regarding slice and dice data of one or more brands.

FIG. 8F illustrates, according to one embodiment, a user interface pageshowing information regarding a theme cloud (negative) of one or morebrands.

FIG. 8G illustrates, according to one embodiment, a user interface pageshowing information regarding a word cloud (negative) of one or morebrands.

FIG. 8H illustrates, according to one embodiment, a user interface pageshowing information regarding a theme cloud (positive) of one or morebrands.

FIG. 8I illustrates, according to one embodiment, a user interface pageshowing information regarding a word cloud (positive) of one or morebrands.

FIG. 8J illustrates, according to one embodiment, a user interface pageshowing information regarding my data upload.

FIG. 8K illustrates, according to one embodiment, a user interface pageshowing further information regarding my data upload.

FIG. 8L illustrates, according to one embodiment, a user interface pageshowing information regarding my modules.

FIG. 8M illustrates, according to one embodiment, a user interface pageshowing further information regarding my modules.

FIG. 8N illustrates, according to one embodiment, a user interface pageshowing information regarding my codebooks.

FIG. 8O illustrates, according to one embodiment, a user interface pageshowing further information regarding my codebooks.

FIG. 8P illustrates, according to one embodiment, a user interface pageshowing information regarding routing rules.

FIG. 8Q illustrates, according to one embodiment, a user interface pageshowing information regarding manage brands.

FIG. 8R illustrates, according to one embodiment, a user interface pageshowing further information regarding manage brands.

FIG. 9 is a flowchart of a process performed by the systems of theprevious Figures for identifying an emotion, a cognitive state, asentiment, or other attribute associated with a document, according toan exemplary embodiment.

FIG. 10 is a flowchart of a process performed by the systems of theprevious Figures for deriving grams and categories from the text of adocument, according to an exemplary embodiment.

FIG. 11 is a flowchart of a process performed by the systems of theprevious Figures for validating and correcting extracted grams,according to an exemplary embodiment.

FIG. 12 is a flowchart of a process performed by the systems of theprevious Figures for assigning grams to driver groups, according to anexemplary embodiment.

FIG. 13 is a flowchart of a process performed by the systems of theprevious Figures for determining a construct score for a document,according to an exemplary embodiment.

FIG. 14 is a flowchart of a process performed by the systems of theprevious Figures for determining a thoughtfulness score for a document,according to an exemplary embodiment.

FIG. 15 is a flowchart of a process performed by the systems of theprevious Figures for determining an impactfulness score for a document,according to an exemplary embodiment.

DETAILED DESCRIPTION

Referring generally to the Figures, a system for conducting aparallelization of tasks is shown and described. That is, unlikeconventional parallelization techniques, which only parallelize at thethread level or the data level, an entire task to be completed isparallelized with another task to be completed. In an exemplaryembodiment, the system can receive messages that include arepresentation of the logical flow of tasks to be processed. Themessages also include a payload content (e.g., a paragraph of text to beprocessed). The system, upon parsing the received representation of thelogic, can identify two (or more) tasks to be completed in parallelrelative to the payload content of the message. The system can thenrespectively distribute the two tasks to two parallel processing units.Each parallel processing unit can complete the entirety of its taskindependently of and asynchronously with the processing of anotherparallel processing unit's task or tasks.

Referring now to FIG. 1, a system for conducting parallelization oftasks is shown, according to an exemplary embodiment. The system isshown to include message sources 101. Message sources 101 transmit amessage that includes a representation of the logical flow of tasks tobe processed. The message also includes a payload content (e.g., aparagraph of text to be processed, an image to be processed, a link todata to be processed, a pointer to data to be processed, etc.).

Framework manager 105 receives the message from the message sources 101.Framework manager 105 then distributes the message to the parallel grid107. The parallel grid 107 can parse the message, including therepresentation of the logic, and can identify two (or more) tasks to becompleted in parallel. The tasks conducted may be to process ortransform the payload content of the message. Details of the parallelgrid 107 will be described in greater detail with respect to subsequentFigures and paragraphs.

The processing tasks described in the representation of the logical flowof tasks to be processed may be defined in a plug-in library accessibleto all nodes of the processing grid 107. In other words, when a task isto be conducted by a node of processing grid 107, the node can fetch (orotherwise access for action) the appropriate plug-in from the plug-inlibrary 109. In some embodiments, each plug-in is a single-purposeplug-in. Model shop content or other resources can be retrieved frommodel shop 111. Parallel grid 107 can thus be able to adapt to differenttypes of logic representations in the messages received for processing.Nodes of the parallel grid, in an exemplary embodiment, are notpreconfigured to handle only a small set of tasks. Rather, the nodes canbe configured “on the fly” using the content of the plug-in library 109and/or model shop 111. For example, a node can be configured to generatea message “on the fly” and perform other operations. Additional detailregarding the plug-in library 109 will be provided in later paragraphs.

Output from the parallel grid 107 can be provided to any selectedoutput. The output may be, for example, specified by the message fromthe message source 101. For example, the parallel grid 107 can providedata to a distributed file system 113 for storage. The parallel grid 107can provide data to application services 117. Data provided toapplication services 117 may be provided to applications that provideuser interfaces (e.g., graphical user interfaces) for outputtingprocessing results (separately and/or in the aggregate) to users (e.g.,users connected to the servers via client browsers or otherwise).Application services 117 may run on a single server or on a combinationof servers.

Application services 117 includes a variety of services which mayinteract with a client or user. Some embodiments may include privateapplications 147 and public applications 145. Private applications 147may have limited access to a select group of users. The select group ofusers may include a subset of a user or client institution. Publicapplications 145 may be accessible by members of the public or a less orunrestricted group of members belonging to the user or clientinstitution. Application services 117 may also include text scoringapplications 144. In some embodiments, text scoring applications 144 mayprovide a score of the payload of a message. The score may be related toa parameter such as an estimated emotion or estimated behavioralcorrelate as discussed in later paragraphs. Application services 117 mayfurther include monitoring service 143. Monitoring service 143 mayprovide a client or user with results relating to, for example,monitoring the emotion of a data source. For example, social listeningand response may provide a client or user with information related topositive or negative changes in emotions corresponding to a particularbrand or product. The monitoring service 143 may further suggest acourse of action in response to the changes identified through sociallistening. More detail will be provided in later paragraphs. Applicationservices 117 may further include a sentiment analysis application 142.In some embodiments, sentiment analysis application 142 may provide tothe client or user information regarding the estimated emotions,estimated behavioral correlates and/or emotional score associated withthe payloads corresponding to a particular brand or product. Forexample, sentiment analysis application 142 may provide informationregarding the percentages of each estimated emotion and/or estimatedbehavioral correlate associated with the payloads of a particular brandor product. Application services 117 may also include MRA applications141. MRA (i.e., marketing research applications) may allow a user tocompare different data sources (e.g., by emotion) to determine whichmarketing approach has been more effective. MRA may also allow a user tocategorize data sources to determine which attributes to a brand aremost appealing.

MRA applications 141 analyzes verbatim responses to open-ended surveys.The MRA applications 141 break down surveys into questions, and theirassociated responses and analyzes them in real-time. A raw data file,which may include branching and skip logic questions and any relatedmetadata, is uploaded. The number of categories responses to be assignedis selected, so that responses with multiple attributes can beauto-coded into multiple categories. An automated natural coding processthen clusters responses into groups of related documents. Merging,renaming or deleting clusters is then possible. MRA applications 141allow for a user to dynamically create and apply code-frames to surveyresponses so all responses can be coded into the categories the analystis most interested in monitoring. The analysis may be further analyzedusing the system and methods described herein to measure sentiment,emotion, context, psychological profiles and other calculatedcharacteristics of the survey responses.

Output from parallel grid 107 may be displayed to a client or userthrough application services 117. In some embodiments, applicationservices 117, and the associated specific services detailed above, mayuse different types of display techniques. For example, applicationservices 117 may display the output of parallel grid 107, or output fromother analysis (e.g. analysis performed by an application service 117)using charts. These may include pie charts, bar graphs, plots, etc. Theoutput displayed may be customizable by the user or client. For example,a user or client may select the type of display and the specificinformation to be displayed using a public application 145 or privateapplication 147. The output display may also include figures. Forexample, the output displayed by the application services 117 mayinclude venn diagrams, word maps, word charts, word clouds, etc.

In an exemplary embodiment, output from the parallel grid 107 cantemporarily be returned to framework manager 105 for further handlingand distribution of tasks. In an exemplary embodiment, output can alsoor alternatively be provided to a big data interface 115. The big datainterface 115 can facilitate transactions with big data stores 119and/or with distributed file system 113. Big data interface 115 mayroute data, standardize data types, normalize data, index data, and/orperform a similar function to facilitate data transfer.

Big data stores 119 may include client data 161, agency data 163,partner data 165, and/or streaming data 167. In some embodiments clientdata 161 may include data for analysis provided by the user or client.For example, client data 161 may include social media posts from aclient or user webpage. Client data 161 may be related to a particularproduct, brand or time frame. In further example, client data 161 may besocial media posts for a time period corresponding to a product launch,news story, product recall, etc. In some embodiments, agency data 163may include data or information sourced from a third party. For examplethis data may include ratings from a ratings agency, reviews from areviewing website, search trends, focus group results and evaluations,etc. In some embodiments, partner data 165 includes data fromorganizations partnered with the client, user, or provider of thesystem. In some embodiments, big data stores 119 may also includestreaming data 167. Streaming data 167 may include any big data,including the data discussed above, that is collected on a continuous(i.e. streaming) basis. Big data may also include data such as webcookies, breadcrumbs, sales figures, or other data sources which may beprocessed to conduct a target determination (e.g., emotion, marketingeffectiveness, etc.).

Output from parallel grid 107 may also be provided to any or anycombination of search servers 151, files systems 153, triples 155, andrelational databases 157. Search servers 151 may store the output fromparallel grid 107 in a searchable format. Search servers 151 may includean indexer and querying engine. In some embodiments, search servers 151may be any of a Microsoft Search Server, Solr, Autonomy, Google Mini,etc. Search servers 151 may be configured to use any of a variety ofsearch methodologies (e.g., Lucene, elastic, MarkLogic, etc.). Filesystems 153 may include any file system which controls how the output isstored and retrieved. File systems 153 may store information mediumsincluding hard disk, solid state memory, magnetic tape, optical disk,cloud based storage, or any other medium. For example file systems 153may be a New Technology File System storing the output of parallel grid107 on a hard disk. Other file systems 153 may be used including thoseassociated with Microsoft, Linux, Unix, or other operating systems.Triples 155 may store the output of parallel grid 107 in a tuple oflength 3. For example the three components of the triples 155 may be thepayload, an associated emotion, and an associated behavioral correlate.In some embodiments output from parallel grid 107 may be of otherlengths. For example, output may be made to ordered pairs or quadruples.Output from parallel grid 107 may also be made to relational databases157. Relational databases 157 may store the output of parallel grid 107as tables of data items ordered according to a relational model. In someembodiments an SQL database may also be used. The output from parallelgrid 107 sent to any of search servers 151, files systems 153, triples155, and relational databases 157 may be accessed by the distributedfile system 113 and in turn may be accessed by the big data interface115, application services 117, and/or big data stores 119.

Message source 101 can include one or more message feeds 103. Messagefeeds 103 may be streaming feeds (e.g., streaming text, streaming video,streaming audio). Message source 101 may also or alternatively be orinclude one or more non-streaming interfaces. For example, a messagingengine (e.g., on a client computer, on a social media server, etc.) maysend messages as a batch. In other embodiments, the framework manager105 or another component of the system may fetch or get messages from aninbox, a database, a compressed file, or another data source. Messagesource 101 may further include information from application services117. Data may be provided by the client or user through the applicationservices 117. For example, a client or user may input data through aprivate application 147 or a public application 145. This data may be amessage source 101. Message source 101 may include data or informationfrom the distributed file system 113. Message source 101 may includedata or information from big data stores 119.

Message feeds 103 are shown to include an indexing data feed or store121, client data feed or store 123, crawler data feed or store 125,and/or streaming data 127. Indexing data feed or store 121 may includedata from social media, big data, or other sources that has beenindexed. Client data feed or store 123 may include data provided by auser or client. This data may originate from application services 117 orbig data stores 119 as discussed above. Client data 123 may be data fromthe client or user otherwise provided to message source 101. Crawlerdata feed or store 125 may include data acquired by a web crawler. Theweb crawler may be used to index social media websites, review websites,or any other website where the desired data types and sets are likely tobe found. Streaming data 127 may include any data source which isgathered continuously. Message feeds 103 may include data such as webcookies, breadcrumbs, sales figures, etc.

A guaranteed messaging streaming interface 131 can be provided. Theguaranteed messaging streaming interface 131 may be configured toreceive data from message feeds 121-127 and to standardize the data forframework manager 105. In other words, interface 131 may be configuredto standardize the message data from a first standard (from messagefeeds 121-127) to a second standard (for framework manager 105 andparallel grid 107). In one embodiment, interface 131 may implement anadvanced message queuing protocol (AMQP) for implementing a standard forthe messages being transmitted from feeds 121-127 to framework manager105. Examples of implementations of the AMQP are RabbitMQ and Apollo.According to another embodiment, interface 131 may implemented using aJava message service (JMS) for sending messages from message feeds121-127 to framework manager 105.

A brokerless messaging streaming interface 133 can be provided. Thebrokerless messaging streaming interface 133 may be configured to allowany message broker (e.g., message feeds 121-127) to communicate withframework manager 105 independent of language or platform. For example,framework manager 105 with a first language or platform may communicatewith a message feed in a second language or platform via interface 133.In one embodiment, interface 133 may implement a Simple (or Streaming)Text Oriented Message Protocol (STOMP) for enabling the communicationbetween message feeds 121-127 and framework manager 105. One suchexample of an implementation of the STOMP is ZeroMQ.

A high performance asynchronous messaging interface 135 can be provided.The high performance asynchronous messaging interface 135 can managecommunications that take place between the various message feeds 121-127and framework manager 105. More particularly, interface 135 may receivea message from a message feed 121-127. If framework manager 105 is busyor not connected to message source 101, interface 135 may place themessage in a message queue while continuing to receive and process othermessages, without requiring an immediate response to the first messageby framework manager 105. Examples of an implementation of interface 135include JActor and Akka.

Referring now to FIG. 2, a simplified block diagram of the parallel grid107 is shown, according to an exemplary embodiment. The parallel grid107 is shown as receiving a simplified exemplary message 201. As shown,exemplary message 201 includes a mark-up language representation of alogical flow of tasks to be completed. Exemplary message 201 is shown toinclude two sequential process tasks to be completed, HTML_CLEANER andVALIDATE. In message 201, HTML_CLEANER and VALIDATE are marked assequential via the <SEQ-PROCESS> tag. The markup <STEP id=1> is used toindicate that HTML_CLEANER and VALIDATE are sequential steps to beassociated with the identifier of 1. The system can receive and parsethe message 201, sending the message to process manager unit 203 ofparallel grid 107. The system may send the message 201 to processmanager unit 203 with an indication that step id=1 is the step forexecution by process manager unit 203.

Process manager unit 203 can cause HTML_CLEANER to be executed by usinga process unit 205. Process manager unit 203 can send the contentpayload from the message (e.g., a set of social media posts) along withan indication of the process task to be completed (e.g., HTML_CLEANER).Upon receiving such information, for example, process unit 205 canrecall instructions for HTML_CLEANER from plug-in library 109.

Plug-in library may contain scripts (e.g., Javascript, Perl, etc.),compiled executable modules, compiled bytecode for execution on avirtual machine environment (e.g., .class, .jar), pre-compiled classfiles, source files (e.g., .java, .py, etc.). One example for theHTML_CLEANER plug-in is shown in FIG. 4.

When the first process is complete, process unit 205 can return theupdated content payload to the process manager unit 203. Process managerunit 203 can update the content payload in the message itself or in anintermediate data structure used for processing the messages. In variousembodiments, one or both of process manager unit and process unit 205may be configured to update the content payload. Because HTML_CLEANERand VALIDATE tasks are marked with the sequential tag, VALIDATE iscaused to run on an available process unit (e.g., any of process units205-207) using the content payload cleaned by HTML_CLEANER.

Upon completion of the VALIDATE task, the process manager unit 203 canthen continue to the parallel processing portion of the message 201. Asshown in the example message 201 of FIG. 2, the mark-up PAR-PROCESS isused to indicate a parallel process. Step 2 is defined to include thetasks of SENTIMENT, CONTENT, THEME, INFLUENCE, and EMOTION. These tasknames may correspond to a task for judging the sentiment of a socialmedia post, categorizing the content of the social post, categorizing atheme for the social media post, judging the influence of the socialmedia post, and ranking the emotion of the social media post.

In some embodiments, the process manager unit 203 and/or the frameworkmanager 105 can function as a framework message router. For example, theprocess manager unit 203, upon parsing the PAR-PROCESS section of themessage 201, can access state, utilization and/or historical informationfor its own process units 205-209 to determine how to distribute theparallel tasks to be completed. For example, the process manager unit203 can distribute the SENTIMENT task (along with the HTML cleaned andverified content payload) to process unit 205, CONTENT task to processunit 207, and THEME task to process unit 209. The final two tasks to beprocessed can be passed to other process manager units and process unitsof the parallel grid 107. As illustrated in the parallel grid 107 ofFIG. 2, a group 225 of processing resources may be used to complete thetasks INFLUENCE and EMOTION. In an exemplary embodiment, the processmanager unit 203 uses utilization or state information regarding otherprocess manager and process unit sets to determine which processmanger/process unit set should receive the tasks INFLUENCE and EMOTION.Alternatively, or in conjunction with such local decision making, theframework manager 105 can assist with the distribution process when oneprocess manager unit's resources are utilized. For example, theframework manager can be queried by the process manager unit 203 todetermine which other process manager unit should receive the overflowtasks. In yet other embodiments, process manager units can be configuredto keep all tasks of a parallel process such that overflow tasks aredone when local process unit resources permit.

Referring now to FIG. 3, a flow chart of a process 300 for parallelizingtasks utilizing the system described in FIGS. 1 and 2 is shown,according to an exemplary embodiment. Process 300 is shown to includebuilding a message (step 302). A simplified example message 312 isprovided on the right side of FIG. 3 to provide an illustrationassociated with step 302. As described with reference to FIG. 1, themessage 312 may be created on-demand by an activity of a userapplication, by a crawler process (e.g., configured to scrape data forprocessing from a social media site), by a messaging interface, by acombination of such elements, and/or in conjunction with one or moredatabases.

Referring still to FIG. 3, process 300 is further shown to includereceiving and parsing the message (step 304). The receiving and parsingmay be completed entirely by the framework manager 105. Alternatively,the receiving and parsing may be completed only in enough detail torecognize the next step for distribution to the parallel grid 305. In anexemplary embodiment, the framework manager places messages to behandled into a queue.

In some embodiments, over time, the framework manager 105 can receiveinformation from the server nodes (illustrated as S1, S2, S3) of theparallel grid 305. The information can include status (e.g., whetheravailable or not, capacity, percentage of usage, etc.). The frameworkmanager 105 can use this information to determine where to distributethe message. The framework manager 105, can for example, determine thatPMUz on server S2 has available processing capacity and decide to routemessage 312 to PMUz.

Once messages are received, the framework manager 105 can distributetasks for parallel processing (step 306) by a plurality of servers(e.g., S1-S3) and by a plurality of processing manager units (PMUs). Asshown in FIG. 3, servers S1-S3 and the PMUs within the servers S1-S3 areavailable to receive parallel tasks from the framework manager 105.

At PMUz running on server S2, the tasks of the message are thendistributed to parallel processing units (step 308). As shown in FIG. 3,for example, Task A is provided to PU1 as Task B is provided to PU2. Inan exemplary embodiment, Task A is completed independently andasynchronously from Task B at the task processing level, each processingunit PU1, PU2 completing their own task naturally, as server resourcesallow. The parallel processing units to which the tasks are distributedmay include available and/or unavailable processing units. In someembodiments, the processing manager can assign higher processing loadtasks to processing units configured to have higher resource allocationsthan other processing units on the same server. In other embodiments,such prioritization is not done. In some embodiments, each processingunit is generated on the fly as tasks are created. In such anembodiment, for example, when PMUz distributes parallel tasks to PU1 andPU2, PMUz also causes processing units PU1 and PU2 to be created. Oncecreated, the tasks run in parallel.

Referring still to FIG. 3, process 300 is further shown to include, ateach processing unit, fetching an appropriate task plug-in from taskplug-in library 109 and executing the task on the message payload (step310). As illustrated in FIG. 3, the processing unit PU1 can pass theresult back to the process management unit PMUz. Passing this resultback may include transmitting the result, populating a database recordon PMUz, and/or conducting any other suitable transmission of resultinformation to PMUz. In an exemplary embodiment, PU1 places the resultdata back into the message and transmits the message with updated stateand result information back to PMUz. Alternatively, PU1 sends the resultback to PMUz and PMUz places the result data back into the message withupdated state information. PMUz can gather the results of each task(e.g., Task A and B). When PMUz has completed both Task A and Task B,PMUz may continue to parse the message 312. Eventually PMUz will reachthe sequential step SEQ-STEP Output_DFS. The task associated withOutput_DFS may be run by, for example, PU1. Output_DFS may includeoutputting the results of Task A and Task B to a distributed filesystem. Other sequential or parallel steps may be used to completeadditional or alternative output steps. For example, in parallel with orin sequence with the output of information to a distributed file system,the message may contain a task of outputting the result to a web servicefor display.

Referring now to FIG. 4, an example of the source code for theHTML_CLEANER plug-in (e.g., as referenced in FIG. 2) is shown. Plug-insutilized by the process units can import packages including classes orother resources for use. Each processing unit PU uses the plug-in toinstantiate the proper class or classes at runtime to support therequested task.

As the example of FIG. 4 illustrates, the plug-in imports the Jsouppackage and the jsoup.safety.Whitelist package, in addition to otherpackages having resources which support the example plug-in. Theillustrated plug-in is written in Scala and generally gets the contentpayload of the message, uses the Jsoup.clean function to clean thecontent payloads, and returns the content payload. The plug-in is shownas also outputting a result string of “success,” which may be used bythe PU or PMU.

Referring now to FIG. 5, another example message for supporting theparallelization is shown. The message includes a representation of thelogic to be completed with respect to a payload. The payload is definednear the bottom of the message. As shown, the payload can be defined byan identifier and/or metadata. The data may be embedded in the messageitself. In other situations the data is included with the message in theform of a pointer or link to an accompanying file or a separate datastore. As shown, the message itself can include header information suchas client identifier, project identifier, a timestamp, otheridentifiers, and a result queue identifier. The task logic is specifiedto have two sequential tasks (e.g., HTML_CLEANER, DOCUMENT_VALIDATE) tobe run prior to the parallel tasks (e.g., LINK_CRAWLER,ENTITY_EXTRACTOR, RELATIONSHIP_DISCOVERY, DEMOGRAPHIC_APPEND, and CORPUSIDENTIFIER).

Referring now to FIG. 6A, a block diagram of a parallel computing device603 that can be used in the parallel processing grid of the previousFigures is shown and described, according to an exemplary embodiment.The parallel computing device 603 may correspond with a server thatforms a part of the parallel grid. Advantageously, the parallelcomputing device 603 does not need to be a part of a server bank. Theparallel computing device 603 can run on a computer (e.g., laptop,desktop, remote server, etc.) that is distributed from a centrallocation or server bank. In some embodiments, parallel computing device603 is or includes purpose-build hardware (i.e., hardware built for aspecific application or purpose).

Communications interface 605 may be any type of communications interfaceconfigured to communicate with at least the upstream framework managerpreviously described. For example, the communications interface may be awireless networking device (e.g., WiFi, Zigbee, Bluetooth, etc.), awired interface (e.g., Ethernet, USB, Firewire, etc.), and/or can be aslot/plug-in interface (e.g., in an embodiment where the computing unitcoupled to a server bank via a blade configuration). The communicationsmay thus be local communication or memory bus communications, wirelesscommunications, Internet communications, and/or wired communications.

Parallel computing device 603 further includes an I/O interface 604. I/Ointerface 604 may be or include a serial or parallel port interface, awireless interface, a USB interface, a display interface, a keyboardinterface, and/or any other type of I/O interface. In embodiments wherethe parallel computing device 603 is a blade computer, interface 604might not be present. In embodiments where the parallel computing device603 is a laptop computer, the I/O interface 604 may include a display, atouchpad, and/or other interfaces found on laptop computers.

Parallel computing device 603 is further shown to include a processingcircuit 608 including a processor 610 and memory 612. Processor 610 maybe, or may include, one or more microprocessors, application specificintegrated circuits (ASICs), circuits containing one or more processingcomponents, a group of distributed processing components, circuitry forsupporting a microprocessor, or other hardware configured forprocessing. Processor 610 is configured to execute computer code storedin memory 612 to complete and facilitate the activities described hereinwith respect to the parallel computing device (e.g., process managerunit with processing units).

Memory 612 can be any volatile or non-volatile computer-readable storagemedium capable of storing data or computer code relating to theactivities described herein. For example, memory 612 is shown to includemodules which are computer code modules (e.g., executable code, objectcode, source code, script code, machine code, etc.) configured forexecution by processor 610. According to some embodiments, processingcircuit 608 may represent a collection of multiple processing devices(e.g., multiple processors, etc.). In such cases, processor 610represents the collective processors of the devices and memory 612represents the collective storage devices of the devices. When executedby processor 610, processing circuit 608 is configured to complete theactivities described herein as associated with parallel computing device603.

Hard disk storage 606 may be a part of memory 612 and/or used fornon-volatile long term storage in the parallel computing device 603.Hard disk storage 606 may store local files, temporary files, a queue ofmessages, tables used for processing, compilers, an operating system,and any other component for supporting the activities of the parallelcomputing device 603 described herein.

Memory 612 is shown to include process manager unit 614. Process managerunit 614 corresponds with process manager unit 203 of parallel grid 107shown in previous figures, according to various exemplary embodiments.Process manager unit 604 receives messages (e.g., message 201 of FIG. 2,messages from framework manager 105), parses the messages, anddistributes parallel tasks to processing units 636 (e.g., shown in FIGS.2, 3, etc.).

Process manager unit 614 is further shown to include an identifier 616.Identifier 616 can be an alphanumeric string or other value for uniquelyidentifying the process manager unit 614. It should be noted that eachparallel computing device 603 may include multiple process manager units614 having unique identifiers. In an alternative embodiment theidentifier 616 may be a unique identifier associated with processor 610or communications interface 605 (e.g., an IP address, a mac address,etc.). The framework manager 105 may use the unique identifiers of thevarious parallel computing devices 603 to keep track of which devicesare available, which devices are a part of the parallel computing grid,and/or to assist with the appropriate distribution of tasks.

Message parser 618 is configured to parse the logic representations ofthe messages (e.g., message 201 of FIG. 2, messages from frameworkmanager 105, etc.). When message parser 618 encounters a message havingparallel tasks, message parser 618 distributes the tasks to availableprocessing units of the processing unit stack 636 using task distributor622. Task distributor 622 can use resource information of the processingunit stack 636 to determine how to distribute parallel tasks. Taskdistributor 622 may use historical task-to-load information to estimatehow intensive a task will be. In an exemplary embodiment, taskdistributor 622 can adjust its task distribution scheme depending on thetypes of tasks, the current processing load of the parallel computingdevice, and/or the average time to completion of tasks.

Processing unit stack 636 can be self-managing (e.g., as new processingunits are needed, processing unit stack 636 can create a new processingunit for use by the process manager unit). In other embodiments,processing unit stack 636 is managed by process manager unit 614.Resource monitor 614 can be used by process manager unit 614 to adaptthe number of allocated processing units. Cleanup module 624 can conductany necessary garbage collecting, reduction of processing units when notnecessary, or other clean-up tasks.

Process manager unit 614 includes resource monitor 620. Resource monitor620 may periodically check the available resources of processing unitstack 636. Resource monitor 620 may also monitor the number of tasksrequired to be completed. Resource monitor 620 may interface with taskdistributor 622 to optimally assign tasks to processing units withinprocessing unit stack 636. Temporary storage module 634 may providetemporary storage tasks, payloads, outputs, etc. required for processingcircuit 608. Process manager unit 614 may control access to temporarystorage 634 for processing unit stack 636. Memory 612 also includes alocal plug-ins and resources module 632. Local plug-ins and resourcesmodule 632 may store the plug-in retrieved from the plug-in library 109.In some embodiments, local plug-ins and resources module 632 may alsostore models retrieved from the model shop 111. Processing unit stack636 may access local plug-ins and resources module 632 as required tocomplete tasks using the corresponding plug-in or model. Memory 612 alsoincludes queue check module 626. Queue check module 626 is used to checkthe tasks assigned to the parallel computing device. In someembodiments, queue check module 626 may also check the queue of eachprocessing unit in the processing unit stack 636. Queue check module 626may be accessed by process manager unit 614 in order to assignoutstanding tasks to processing units within the processing unit stack636 and to keep track of tasks assigned to the parallel computing device603. Memory 612 also includes configuration module 628. Configurationmodule 628 may be accessed by process manager unit 614 in order toconfigure the processing circuit 608 to perform the assigned task. Forexample, configuration module 628 may be accessed by process managerunit 614 in order to determine the correct plug-ins to retrieve from theplug-in library for a particular task.

FIG. 6B is a block diagram of a server device 653 including a frameworkmanager 664 that can be used with the parallel computing device andprocessing grid of the previous Figures, according to an exemplaryembodiment. The server device 653 may correspond with a server thatforms a part of the parallel grid. The server device 653 mayalternatively run on a computer (e.g., laptop, desktop, remote server,etc.) that is distributed from a central location or server bank. Theserver device may be a parallel computing device containing theframework manager.

Communications interface 655 may be any type of communications interfaceconfigured to communicate with at least the upstream framework managerpreviously described. For example, the communications interface 655 maybe a wireless networking device (e.g., WiFi, Zigbee, Bluetooth, etc.), awired interface (e.g., Ethernet, USB, Firewire, etc.), and/or can be aslot/plug-in interface (e.g., in an embodiment where the computing unitcoupled to a server bank via a blade configuration). The communicationsmay thus be local communication or memory bus communications, wirelesscommunications, Internet communications, and/or wired communications.

Server device 653 further includes an I/O interface 654. I/O interface654 may be or include a serial or parallel port interface, a wirelessinterface, a USB interface, a display interface, a keyboard interface,and/or any other type of I/O interface. In embodiments where the serverdevice 653 is a blade computer, interface 654 might not be present. Inembodiments where the server device 653 is a laptop computer, the I/Ointerface 654 may include a display, a touchpad, and/or other interfacesfound on laptop computers.

Server device 653 is further shown to include a processing circuit 658including a processor 660 and memory 662. Processor 660 may be, or mayinclude, one or more microprocessors, application specific integratedcircuits (ASICs), circuits containing one or more processing components,a group of distributed processing components, circuitry for supporting amicroprocessor, or other hardware configured for processing. Processor660 is configured to execute computer code stored in memory 662 tocomplete and facilitate the activities described herein with respect tothe server device (e.g., framework manager unit with modules).

Memory 662 can be any volatile or non-volatile computer-readable storagemedium capable of storing data or computer code relating to theactivities described herein. For example, memory 662 is shown to includemodules which are computer code modules (e.g., executable code, objectcode, source code, script code, machine code, etc.) configured forexecution by processor 660. According to some embodiments, processingcircuit 658 may represent a collection of multiple processing devices(e.g., multiple processors, etc.). In such cases, processor 660represents the collective processors of the devices and memory 662represents the collective storage devices of the devices. When executedby processor 660, processing circuit 658 is configured to complete theactivities described herein as associated with server device 653.

Hard disk storage 656 may be a part of memory 662 and/or used fornon-volatile long term storage in the server device 653. Hard diskstorage 656 may store local files, temporary files, a queue of messages,tables used for processing, compilers, an operating system, and anyother component for supporting the activities of the server device 653described herein.

Memory 662 includes framework manager 664. Server device 653, usingframework manager 664, receives messages from the message source.Framework manager 664 temporarily stores messages from the messagesource in message queue 666. Framework manager 664 uses resource manager668 in conjunction with communications interface 655 and I/O interface654 to determine how to allocate tasks associated with the messages.Tasks may be allocated to parallel computing devices 603 within parallelgrid 107 based on the resources available to each parallel computingdevice 603 and the queue of tasks already assigned. Framework manager664 includes distributor 670. Server device 653 uses distributor 670along with I/O interface 654 and communications interface 655 to sendmessages and associated tasks to each parallel computing device 603.Framework manager 664 also includes administrative interface 672.Administrative interface 672 may be used to allow a client or user tochange the parameters of framework manager 664. For example,administrative interface 672 may be configured to allow a client or userto determine the amount of resources to be used to handle tasks. Thismay constitute a portion of all the parallel computing devices 603available. Administrative interface 672 may also be configured to allowa client or user to prioritize tasks such that certain tasks areperformed first or sooner than others. Administrative interface 672 maybe configured to allow a client or user to select particular computingresources to handle particular tasks. In some embodiments,administrative interface 672 may be used by a client or user to selectwhich plug-ins will be used for a particular task or which plug-insand/or models are made available to parallel computing devices 653.

Memory 662 of server device 653 further includes several modules. Serverdevice 653 includes a message feed module 680. Message feed model 680 isconfigured to retrieve the messages from the message source. In someembodiments, message feed module 680 may be configured withadministrative interface 672. Message feed module 680 may be configuredto retrieve or ingest messages only from particular message sources.Message feed module 680 may also be configured to only retrieve messagesmeeting certain user or system defined parameters. Memory 662 alsoincludes a guaranteed message streaming interface 681. As describedabove, guaranteed message streaming interface module 681 is configuredto receive messages, from the message feed module 680 in thisembodiment, and is configured to standardize the data within the messagefor use by the framework manager 664. Standardizing the data may includestandardizing the message data from a first standard to a secondstandard. In this embodiment, memory 662 further includes a brokerlessmessaging streaming interface 682. As previously described, brokerlessmessaging streaming interface module 682 allows any message broker tocommunicate with the framework manager 664 as executed by the serverdevice 653. Multiple languages or platforms may be used when theframework manager 664 communicates with the message feed module 680. Insome embodiments, and as is shown, memory 662 includes a highperformance asynchronous messaging interface module 683. Server device653 may use the high performance asynchronous messaging interface module683 to control communication between the message feeds and the serverdevice 653 running the framework manager 664. The high performanceasynchronous messaging interface module 683 may be used to place amessage in the message queue 666 if the framework manager 664 isotherwise busy or not connected to the message source.

In the illustrated embodiment, server device 653 includes severalmodules in memory 662 which may be used to control output from and inputinto the framework manager 664 running on server device 653. Serverdevice 653 may include an application services module 684 as discussedpreviously and in following paragraphs. Application services module 684may be used by server device 653 to display outputs from the parallelgrid. In some embodiments, application services module 684 may befurther used by server device 653 to provide inputs into the frameworkmanager 664. For example, a client or user may input data through anapplication service such as a private application 147 which may be runby application services module 684 on server device 653. This data maythen be handled by the framework manager running on server device 653.Server device 653 may also include a big data interface module 685. Bigdata interface module 685 facilitates output to big data stores andinput from big data stores to the framework manager 664. Server device653 may further include a distributed file system interface module 686.Distributed file system interface module 686 may be used by serverdevice 653 and framework manager 664 to retrieve data from thedistributed file system 113. This data may be used as an input into theframework manager 664 for distribution to the parallel grid 107. Theapplication services module 684, big data interface module 685, anddistributed file system interface module 686 may also be used by theframework manager 664 to designate an output destination for parallelgrid 107 and the parallel computing devices 603 therein.

The illustrated embodiment also includes four modules for controllingthe output of parallel grid 107. Memory 662 includes search serversmodule 687. Search servers module 687 may be used by server device 653to designate that parallel grid 107 output to search servers 151. Searchservers module 687 may further be used to control the parameterscorresponding to the operation of the search servers 151. For example,search servers module 687 may be used to designate which search server151 to output to or the architecture of the output. Memory 662 alsoincludes a file systems module 688. File systems module 688 may be usedby server device 653 to designate that parallel grid 107 output to aparticular file system 153. For example, file systems module 688 may beused by the framework manager 664 and server device 653 to causeparallel grid 107 to output to the file systems 153 using a particularfile type. For example, New Technology File System may be used. Triplesmodule 689 of the illustrated embodiment controls the parametersassociated with parallel grid 107 output to triples 155. For example,triples module 689 may set the three parameters to be output to triples155. For example, the output to triples 155 may be set to include thepayload, an associated emotion, and an associated behavioral correlate.Memory 662 also includes a relational databases module 690. Relationaldatabases module 690 may set the parameters for output to relationaldatabases 157. For example, relational databases module 690 may beconfigured to provide for output of parallel grid 107 to relationaldatabases 157 and control what values are output. For example, thepayload and estimated emotion may be the only outputs, or the outputscould include the estimated behavioral correlate as well. These fourmodules, the search servers module 687, file systems module 688, triplesmodule 689, and relational database module 690, may also be configuredto provide for inputs into the framework manager run by server device653.

FIG. 7A is a flowchart illustrating the steps for processing messagepayload content to determine emotional scores and behavioral correlates,according to an exemplary embodiment. Process 700, using the componentsand steps discussed above, ingests data 708, performs analytics 716,conducts emotional and behavior processing 722, and generates actionableinsights 728. Data sources for process 700 may include any of socialmedia 702, big data 704, or research, surveys, or transcripts 706. Thesedata sources may be provided by the client or user, third party datacollection and/or analysis services, or retrieved from social mediasources. For example, social media 702 data may include posts fromsocial media websites or other information generated by web crawlerstargeted towards social media providers. Big data 704 may be provided bya client or user or may be acquired from third parties. Big data 704 mayalso be generated from partners and streaming sources. For example, bigdata 704 may include an archive of social media posts maintained by aclient or user for a given period of time. This period of time may be ofany length or may be targeted. In some embodiments, the client or usermay provide an archive of social media posts spanning a time framecorresponding with a new product launch. Such data would be ingested asbig data 704. Research, surveys, or transcripts 706 may also provide asource of data for process 700. Any data source may be generated or maybe provided by a client or user. In some embodiments, the client or usermay provide data for analysis by process 700 through applicationservices 117, 684. For example, a client or user may provide data foranalysis through a private application 147 which may be accessed via abrowser in some embodiments.

Process 700 ingests any data source 708 including social media 702, bigdata 704, and research, surveys, transcripts 706. The data is firstingested 710. Data is gathered from sources including social media 702,big data 704, or research, surveys, or transcripts 706. The dataingested is then processed by applying linguistic filtering 712.Linguistic filtering 712 may include determining that the language ofthe post (e.g. English, French, German, etc.) corresponds to therelevant market for which process 700 is being applied. Linguisticfiltering 712 may also include ensuring that payload content (e.g., aparagraph of text to be processed from a social media post) is generatedby a consumer rather than a merchant or automated source. Disambiguationfiltering 714 is also applied to the payload. Disambiguation filtering714 may filter out payload content with multiple possible meanings. Insome embodiments, disambiguation filtering 714 may elect a meaning andassign it to the content of the payload. The combination of applyinglinguistic filtering 712 and applying disambiguation filtering 714 iningesting any data source 708 may include preprocessing, cleaning,harmonizing, or normalizing of the payload.

The ingested data source and corresponding payload are analyzed usinganalytics 716. Analytics 716 may include one or both of ANLP Analytics718 and custom purpose analytics 720. Analytics 716 may further includeplug-ins and model shop content. ANLP Analytics 718 and custom purposeanalytics 720 are accessed by the components of parallel grid 107 inorder to analyze the payload as required by the tasks included in themessage (e.g. to process or transform the payload content of themessage).

The analyzed payloads may then be further processed to estimate theemotions associated with the payload and to estimate the behavioralcorrelate to the emotion. Emotional and behavior processing 722 includesestimating emotions 724 and estimating the behavioral correlates 726. Toestimate emotions 724, components of parallel grid 107 apply plug-insfrom the plug-in library 109 and/or models from the model shop 111 tothe payload. This results in an estimated emotion associated with thepayload. For example, the payload may be estimated to contain theemotion anger. In some embodiments, multiple emotions may be estimatedto correspond to a payload. These emotions may be scored or assigned avalue relative to the perverseness or strength of the emotion in thepayload. In some embodiments, the emotion with the highest score may beassigned to the payload. In other embodiments, or depending on the needsof the client or user, the emotion with the highest score will beassigned to the payload and all other estimated emotions will bedisregarded. To estimate behavioral correlates 726, components ofparallel grid 107 apply plug-ins from the plug-in library 109 and/ormodels from the model shop 111 to the payload. This results in anestimated emotional correlate corresponding to the payload and theemotion corresponding with the payload. For example, the payload may beestimated to contain the emotion anger and be further estimated to beassociated with the emotional correlate of likely to return the product.

The payload may be further processed by process 700 in order to generateactionable insights 728. In some embodiments, process 700 generatesactionable insights 728 through the use of the components of parallelgrid 107, plug-ins from the plug-in library 109, and/or models from themodel shop 111. In some embodiments, process 700 may generate actionableinsights 728 through application services 117, 684. The applicationservices 117, 684 may run independently of parallel grid 107. One ormore application services 117, 684 may run on servers. In someembodiments, application services 117, 684 may be performed as taskswith parallel grid 107 and the associated components including theplug-in library 109 and/or the model shop 111. For example, process 700may predict sales 730, measure performance 734, predict engagement 732,loyalty, and churn, and/or monitor and route 736.

Process 700 may predict sales 730 using emotion scores assigned to thepayload in earlier processes of process 700. One or more emotion scoresassigned to the payload may be used to predict sales 730. Multiplepayloads determined to be related to the same product through process700 may be analyzed by application services 728 to predict sales 730. Insome embodiments, a weighted average of emotion scores may be used orother similar analytic technique (e.g. average emotion score, medianemotion score, distribution of emotion score, modeling of behaviorassociated with emotion score, etc.). In some embodiments, the estimatedbehavioral correlate 726 may be used alone or in conjunction with theestimated emotion 724 to predict sales 730.

Process 700 may measure performance 734. In some embodiments,application services 728 may measure performance 734 using estimatedemotions 724 and estimated behavioral correlates 726 as related tospecific brand attributes. For example, all the payloads determined tobe associated with the client or user's brand may be processed withprocess 700. The resulting estimated emotions and/or estimatedbehavioral correlates may be used by application services 117, 684 toassign one or more brand attributes to the brand being analyzed. In someembodiments, application services 117, 684 may also be used to measureperformance 734 by determining brand awareness. For example, process 700may be used to analyze message feeds 103 related to a particular classof product. The message feeds may be further analyzed to determine whatpercentage or distribution of payloads have an estimated emotion 724and/or estimated behavioral correlate 726 associated with the client oruser's brand. In some embodiments, brand awareness may be furtheranalyzed to determine if the brand awareness is primarily related topositive estimated emotions and positive estimated behavioral correlatesor negative estimated emotions and behavioral correlates. The resultsmay be reported in absolute terms (e.g. positive) or relative terms(e.g. 60% positive, 40% negative). In some embodiments parallel grid 107and the associated components may be used in conjunction with or inplace of application services 117, 684 to measure performance 734.

Process 700 may predict engagement, loyalty, and/or churn 732. Process700 may predict engagement, loyalty, and/or churn 732 by determiningshock and loyalty experiences related to a brand. For example, process700 may determine the number of payloads associated with a particularbrand. Process 700 may further determine the number of payloads in thatset with a positive estimated emotion 724 and/or an estimated behavioralcorrelate corresponding to brand loyalty. Process 700, throughapplication services 117, 684 and/or parallel grid 107 and theassociated components, may predict engagement, loyalty, and/or churn 732based on the percentage, distribution, or strength of the positiveestimated emotions 724 and/or the estimated behavioral correlates 726corresponding to brand loyalty relative to the total number of payloadsassociated with a particular brand. In some embodiments, process 700 maypredict engagement, loyalty, and/or churn 732 through similar analyticprocesses using other estimated emotions 724 and/or estimated behavioralcorrelates 726. For example, the percentage or distribution of negativeestimated emotions 724 and/or negative behavioral correlates 726relative to the total number of payloads associated with a particularbrand may be used to predict engagement, loyalty, and/or churn 732.

Process 700 may also monitor and route 736. In some embodiments, process700 may monitor and route 736 through social listening and response. Forexample, process 700 may be used to determine if a brand or product hasnegative estimated emotions 724 and associated negative estimatedbehavioral correlates 726. Social media data sources may indicate,through process 700, that a product has a substantial number of payloadswith an associated estimated behavioral correlate 726 of likely not topurchase the product again. A client or user may take an action inresponse to the generated actionable insight 728 to attempt to recapturethe consumer's business or loyalty.

Generally, demographic variables are collected from consumers. Forexample, these demographic variables may include age, gender, region,income, etc. The consumers then provide a corpus of comments about theirexperiences with products and services. The same consumers also providecompleted survey which help them to describe thoughts, feelings,behaviors, etc. which relate to their experiences with products andservices described in the comments. The surveys provide a state of mindassociated with the comment and the person who wrote the comment. Usingthis correlation between experience with products, comments, and stateof mind, a computational model is created predicting consumers' state ofmind from comments gathered from other sources. The model uses theassociation between language and emotion. As has been previouslydescribed these comments may gathered from a variety of sources such associal media, big data etc. The computational model just described isimplemented by the system previously described through the plug inlibrary and model shop. The analysis is conducted using the systems andmethods previously described herein.

In some embodiments, models go through a confirmatory analysis prior tobeing implemented through the plug in library and/or model shop.Crowdsourcing techniques are used to validate and refine thecomputational models. In this process, human raters read comments takenfrom sources, such as social media, and rate the emotional stateexpressed in the comment. The human ratings are compared to the ratingsgenerated by the computation model that was developed using the abovedescribed steps. For example, traditional validation methods (97%target) as well as the Turing test may be used. If there is a match,within tolerances, then the model is valid and is used in the analysis.If the there is no match, the model requires refinement.

The models created are scalable and may be used in any context. Themodels may be used for any brand or company. Additionally, the processof creating models and validation is repeated to grow the model andimprove the accuracy of the model with respect to understanding emotionand behavior. Thus the analysis improves over time.

An embodiment of the model creation and validation process isillustrated in FIG. 7B. Incoming data (750) is gathered from a datasource. The incoming data may be comments generated from consumers asdiscussed above in relation to the model generation process. In otherembodiments, the incoming data may come from other sources. For example,the incoming data may be gathered from social media website. Then, it isdetermined whether there are existing models (751). If there are noexisting models, then exploratory analysis (expert observations) isconducted (753). In some embodiments the exploratory analysis isconducted by human raters. As previously described, demographicvariables may be collected pertaining to authors of the data. Corpuscollection takes place (755). This includes gathering comments. In someembodiments, this step may include the creation of additional commentswritten by real consumers about their experiences. The collected corpusis gramulated with gramulation techniques (757). In some embodiments,this process includes breaking the collected corpus and/or commentswithin the corpus down into smaller parts. For example, a comment may bebroken down into individual words to be analyzed. Models are thencreated (759). In some embodiments, the models are created using thetechniques described above. The authors of the corpus of commentscomplete surveys including thoughts, feelings, and behavior which linkthe commenter's state of mind to the comment. The computational model isthen constructed using this association between words of the commentsand the survey results. The model is capable of predicting consumerstate of mind. Once the model has been created (759), data is processedthrough the model (761).

In the case that there are existing models at step (751), the incomingdata is processed through the models (761). The models analyze the datato predict consumer state of mind (e.g. emotion). Patterns are lookedfor in the results (763). For example, patterns which appear to becaused by errors in the model may be identified and the model adjusted.A confirmatory analysis through crowd sourcing is then performed (765).This step may include, as discussed above, human raters assigning anemotion to the same data processed with the model. The results of thehuman raters and the models are compared. Using this comparison, newmodels are created and/or models may be modified (767). For example,models may have additional words with associated emotional states addedto them, additional contextual situations for certain words and theassociated emotions may be provided to the model, etc. The new modeland/or modified model is then crowd sourced to validate the new modeland/or modified model (769). This may include comparing the results forthe emotions attached to each comment from the human raters to theemotions attached to each comment by the new and/or modified models. Thecomments may then be reused as incoming data (750). In some embodimentsadditional new comments may be added as incoming data.

The results of the analysis, using the previously described systems andmethods, are accessed using the previously described applicationservices. These application services may include private applications,public applications, etc. The application services may be implementedthrough a user interface. The user interface may access information fromthe distributed file system or other sources to display to the user theresults of the analysis conducted using the systems and methodsdescribed herein.

FIGS. 8A-8R illustrate an embodiment of a user interface which may allowa user to access the previously described application serves and/orinteract with the systems and methods previously described forconducting the analysis. FIGS. 8A-8R illustrate a variety of pages whichmay be presented to a user according to an embodiment of the invention.The pages are presented to the user through a dashboard having a MissionControl and Command Center allowing for the selection of a brand and themanagement of brands. A particular brand which may be selected using thedrop down window displayed in the upper right hand corner of theinterface. A user may also manage the brands which are selectablethrough the drop down menu by clicking the manage brands button alsolocated in the upper right hand corner of the display. In someembodiments, a user may manage other aspects of the brands using themanage brands button. With general reference to FIGS. 8A-8R, the userinterface/display includes a menu ribbon at the top of the display. Themenu ribbon may be made up of buttons and/or dropdown menus. In someembodiments, the menu ribbon may include hyperlinks. The menu ribbonincludes menus for My Data, My Modules, My codebooks, etc. each of whichwill be discussed with reference to later drawings. The menu ribbonallows a user to access the information associated with each menu and toprovide inputs where relevant. The user interface/display furtherincludes a navigation panel on the left with buttons associated withsummary, sentiment, attribute, word clouds, brand health etc. pages.These pages may be accessed by a user by clicking on the associatedbutton in the navigation panel. These pages are discussed in greaterdetail with reference to the associated figures herein.

FIGS. 8A-8R further illustrate a filed with filter parameters located inthe lower left hand corner of the display. The filter filed includesfields allowing the user to select a date range for which informationwill be displayed about the brand. The filter field further allows theuser to filter the data presented by the attributes of the commenter.For example and as illustrated, a user may select a class of consumerfor which brand data will be displayed (e.g. gender). Multiple gendersmay be selected. In some embodiments, the user may filter theinformation displayed by other categories pertaining to commenters suchas race, ethnicity, age, place of residence, income, etc. The user mayalso select using the drop down menus of the filter field the particularsentiments for which data will be displayed, the primary emotions forwhich data will be displayed, and/or the media type from which commentsoriginated. Users may also use the detect attribute drop down menu tofilter the data used in the analysis by one or more attributes whichwere detected. For example, a user could use the drop down to onlydisplay results for comments which included happiness, excitement, andanger. Furthermore, the user may select the sample size to be used inthe analysis by selecting a sample size from the sample drop down menuin the filter field. In some embodiments, the Is Personal drop down menumay be used to filter the results by the type of comment that is made.For example, the drop down menu may be used to filter out comments whichare made on a brands social media page, published outside of socialmedia, made by corporate social media accounts, etc. In someembodiments, the confidence drop down menu may be used to filter theresults depending on the confidence level attached to the analysis ofeach comment. For example, results which detect an attribute with lowconfidence that the comment is characterized by the attribute may befiltered out of the results.

With continued general reference to FIGS. 8A-8R. FIGS. 8A-8R illustratevarious charts and graphs. In some embodiments, the informationdisplayed may be displayed using a chart or graph other than what isdepicted in the figures. For example, information may be displayed informats such as bar charts, pie graphs, word clouds, line graphs, etc.Furthermore in some embodiments, the information displayed may beorganized in a different manner than what is depicted. For example,higher level detail may be positioned in a column on the left side ofthe display, finer level detail in a middle column, and comparisons in arights column. The illustrations provided in FIGS. 8A-8R also representthe default views provided to users for each page. In some embodiments,the default views may be different. In some embodiments, the defaultviews, which may or may not include filter settings, may be customizedby a user.

FIG. 8A illustrates a Mission Control and Command Center which displaysa variety of brand data to the user. In FIG. 8A information theattribute page is particularly displayed. The attribute information isdisplayed for a particular brand which may be selected using the dropdown window displayed in the upper right hand corner of the interface. Auser may also manage the brands which are selectable through the dropdown menu by clicking the manage brands button also located in the upperright hand corner of the display. The attribute information displayedincludes a top row of higher level information. This includes thepercentage of positive emotions and/or attributes associated with thebrand. The higher level information also includes the percentage ofnegative emotions and/or attributes associated with the brand. In someembodiments, the higher level information displayed in the top row mayinclude percentages of other emotions and/or attributes associated withthe brand. For example, the higher level information may include thepercentage of neutral, ambiguous, surprise, shock, etc. emotions and/orattributes associated with the brand. In some embodiments, theseattribute percentages may be determined with regard to a number ofsocial media posts. As is illustrated the higher level informationdisplayed in the top row may include the average number of social mediaposts per day. In some embodiments, this is the number of posts on abrands social media page. In other embodiments, this may be the numberof posts per day which mention the brand or a product of the brandacross numerous social media pages and/or profiles. Also displayed amongthe higher level information in the top row may be the total number ofsocial media posts. As just described, this may be the total number ofposts on a brand page or, in some embodiments, may be the total numberof posts across multiple social media pages and/or profiles. Alsodisplayed in the top row is the date on which the least mentions of thebrand, or a product of the brand, occurred. Also included with thisinformation is the actual number of mentions. The top row may furtherinclude the date on which the most mentions of the brand, or a productof the brand, occurred. In some embodiments, the top row of high levelinformation may include additional or other information which may bechosen by the user.

Also included is a middle row of charts illustrating finer detail thanthe higher level detail top row. The finer detail charts may illustratethe percentage of various sentiments corresponding to attributes of theselected brand (e.g. the products of the brand). Multiple attributes maybe shown on each chart by color coding the attributes. The finer detailrow may also include a chart illustrating the breakdown by emotion ofattributes associated with a brand. This breakdown may include suchemotions as gratitude, happiness, desire, confusion, etc. associatedwith the brand. Also included in the finer detail row of charts is abreakdown of the media type used in the analysis displayed. The mediatype breakdown illustrates the sources of the information used toanalyze the attributes associated with the selected brand. For example,the media type chart may illustrate the percentages of social mediaposts from a first social networking website, social media posts from asecond social networking website, news stories mentioning the brand,comments on message boards, blog posts, etc. The breakdown by media typemay be color coded so as to show what attributes of a brand receivecomments from which media source.

With continued reference to FIG. 8A, the lower displays comparisoninformation. The comparison information may compare two or more brands,attributes of two or more brands, and/or two or more attributes. In someembodiments, the two or more products are of a same brand. In someembodiments, the two or more products are competing products fromdifferent brands. As is illustrated, brand attributes may be comparedusing a pie chart. Comments about two or more brands may be analyzed fora particular attribute. The share of total positive emotions and/orsentiments for each brand may be displayed using a pie chart. In someembodiments, negative attributes or particular attributes may becompared. Furthermore, in some embodiments other charts may be used todisplay the information (e.g. bar chart). The comparison information mayalso include a chart illustrating the attribute trends for one or moreproducts. The attribute trend may illustrate the number of positiveattribute comments a product receives over time.

FIG. 8B illustrates a Mission Control and Command Center which displaysa variety of brand data to the user. In FIG. 8B, information about thesentiment page is particularly displayed. This page may include a chartillustrating a sentiment summary as depicted in the upper left. Thesentiment summary shows the number of comments associated with eachsentiment. For example, the sentiment summary may illustrate using a bargraph the total number of comments which are positive, negative,neutral, etc. In the top right, the sentiment page displays the postcount by sentiment. The post count by sentiment illustrates the numberof posts of a particular one or more sentiments made over time. Multiplesentiments may be illustrated on the same line graph using differentline types and or different line colors. For example, positive andnegative sentiments may be displayed. In some embodiments one or moresentiments may be graphed including positive, negative, neutral, etc.This may be illustrated using a line graph. The post count by sentimentmay also include an additional line graph which illustrates the numberof comments for one or more sentiments over time where a sub category ofcomments is displayed. For example, a line graph may display onlycomments which are published (e.g. published in a magazine review). Insome embodiments other categories of comments may be displayed inaddition or instead of published comments such as public comments,comments of a certain media type (e.g. only social media comments),comments of wide circulation (e.g. published comments, social mediacomments made by people with a large number of followers, etc.), etc.The sentiment page also includes a breakdown by positive emotion. Thebreakdown by positive emotion provides greater detail to the positivesentiment number displayed in the sentiment summary chart. The breakdownby positive emotion chart illustrates the number of positive sentimentcomments which expressed a sub category of positive sentiment. Forexample, a bar chart shows the number of gratitude comments, happinesscomments, desire comments, etc. The sentiment page also includes abreakdown by negative emotions in the lower left corner. In someembodiments, additional or other breakdowns by emotion may be displayed(e.g. neutral, surprise, etc.) Also displayed on the sentiment page iscontent by sentiment. The content by sentiment may be broken into twofields, one of which shows positive content by sentiment (middle right)and other shows negative content by sentiment (lower right corner). Insome embodiments, the graphs displayed in the content by sentimentfields may be the intensity (e.g. number) of comments over time.

FIG. 8C illustrates a Mission Control and Command Center which displaysa variety of brand data to the user. In FIG. 8C the brand compare pageis particularly displayed. At the top of the display is a series ofcheck boxes which allow the user to select which brands to compare. Alsoincluded is information about the brand such as the brand name, the lastdate for which updated data is available, the total number of posts(comments) associated with the brand, etc. Also included is a compareselected brands button which when pressed by the user will update theinformation displayed below.

A series of charts, for example bar charts, are displayed comparing thebrands. A series of tabs allows the user to further select thecomparison data which is displayed. The negative sentiment tab isdisplayed in the figure. The negative sentiment tab displays bar chartscomparing the percentage of each type of negative sentiment associatedwith each brand. For example, a bar char illustrates the percentage ofnegative sentiment comments are associated with each brand. Thepercentage of confusion sentiment comments associated with each brand isalso. Bar charts display the sub categories of negative sentimentsincluding frustration, anger, disgust, etc. This type of display isrepeated for the sentiments on the other tabs. For example, the positivesentiment tab may include bar charts displaying the percentage ofhappiness, satisfaction, etc. associated with each brand. Tabs alsoprovide charts showing a summary of sentiment by brand (e.g. percentageof all positive sentiments associated with each brand), channelpositive, channel negative, etc. In some embodiments, the summary bybrand tab may include both positive and negative emotion charts for thebrands compared. For example, the summary tab may include descriptiveinformation of each brand as well as charts showing each brandspercentage of anger and happiness emotions. In some embodiments, thechannel positive tab may include charts such as the ones described abovecomparing brands with respect to different distribution channels. Forexample, the happiness and satisfaction of the brands may be illustratedwith respect to in store sales, on line sales, resale, etc. In someembodiments, the channel negative tab shows the negative emotionsassociated with each brand by percentage of each brand as well as bydistribution/sales channel. In some embodiments, the information may bedisplayed using other types of charts and/or figures (e.g. pie charts).

FIG. 8D illustrates a Mission Control and Command Center which displaysa variety of brand data to the user. In FIG. 8D the brand health page isparticularly displayed. The brand health page includes a sectiondisplaying information of weekly trends by media type. Weekly trends bymedia type displays trends of the number of comments about the brand fora given week. Under the media label is the type of media for which thetrend information applies. For example, the type of media for whichtrend data is displayed may include, one or more social media networks,reviews, blog comments, etc. For each social media type the current weekcount of comments (of that social media type) is displayed. Alsodisplayed are the previous weeks count of comments about the brand, theweek to week percentage change in the number of brand comments, and atrend indicator. The trend indicator corresponds to the percentagechange. The trend indicator may be a symbol such as a green arrow, redarrow, gray bar, caution sign, green light, red light, stop sign, iconsof different sizes, etc. Also displayed on the brand heath page is themonthly trends by media. The monthly trends by media type displayssimilar information to that of weekly trends by media type except thatthe information displayed is on a month by month basis rather than aweek by week basis. Also displayed on the brand heath page is thequarterly trends by media. The quarterly trends by media type displayssimilar information to that of weekly trends by media type except thatthe information displayed is on a quarter by quarter basis rather than aweek by week basis. In some embodiments trends by media type may bedisplayed over other time periods. For example, trends by media type maybe displayed on a year by year basis, day by day basis, etc. Alsoincluded on the brand health page is a field which allows the user toset one or more periods for which the preciously discussed data may bedisplayed. The periods may be set by the user typing in a start and/orstop date or by selecting a start and/or stop date using a calendar.

FIG. 8E illustrates a Mission Control and Command Center which displaysa variety of brand data to the user. In FIG. 8E the slice and dice pageis particularly displayed. The slice and dice page includes a resetanalysis grid button which, when pushed by the user, refreshes the gridaccording the preferences input by the user. The user inputs preferencesusing the buttons located beneath the reset analysis grid button. Theuser may alter formulas using the formula button. In some embodimentsformulas may relate to the method in which data is processed to providethe analysis (e.g. parameters affecting how a primary emotion, secondaryemotion, and/or sentiment is attached to a comment). The user may alsoalter the layout of the analysis grid (e.g. what is displayed, in whatorder it is displayed, etc.) using the layout button, sort the entriesin the analysis grid (e.g. by publish date, media type, etc.) using thesort button, filter the data (e.g. exclude social media type comments)using the filter button, etc. using the illustrated buttons. The usermay also alter the way in which data is displayed in the analysis gridusing the group button. For example, certain types of data and/or datapoints may be displayed in a group in the analysis grid. In someembodiments, the user may view a summary of the analysis grid using theaggregate button. The aggregate button may perform additional analysisof data in the analysis grid using a combination of one or may datapoints and/or types (e.g. display further information regarding blogmedia type comments). A user may alter the layout of the analysis gridusing the chart button. For example, a user may determine whatinformation is included in the analysis grid and/or how it is displayed.In some embodiments, pressing/clicking the chart button may generate avisual representation of the data in the analysis grid (e.g. bar charts,pie charts, line graphs, etc.). The crosstab button allows a user togenerate a contingency table. The contingency table may be created bycross tabulation. The contingency table may also be created using astatistical process summarizing categorical data from the analysis grid.A user may alter the way in which the analysis grid is displayed usingthe paging button. The paging button allows the user to determine thenumber of results shown per page of the analysis table. In someembodiments, other display and data options may be available to theuser. The slice and dice display also includes navigation tools allowingthe user to navigate the pages of the analysis grid such as arrowbuttons and a field to jump to a certain page. The slice and dicedisplay also includes buttons to allow the user to export the analysisgrid as spreadsheet, CSV, or PDF. In some embodiments, the analysis gridmay be exported in other file formats (e.g. a word or text document).The slice and dice display also provides the user with the analysisgrid. The analysis grid shows data about individual comments about theselected brand/product. The information displayed about the commentsincludes the date on which the comment was made, content, the media typeof the comment, word count, emotions, the author, etc. In someembodiments, additional information may be included (e.g. the gender ofthe author). The content link column includes a link to the text of thecomment.

With respect to FIGS. 8F-8I, the figures illustrate the word and themecloud page. The word and theme cloud page includes a row of tabs nearthe top of the page. Each tab allows a user to view word cloudsassociated with a particular measured value. As illustrated, the tabsinclude a negative and positive theme cloud labeled theme cloud(negative) and theme cloud (positive) and a negative and positive wordcloud labeled word cloud (negative) and word cloud (positive). In someembodiments, additional values, attributes, emotions, comment phrase,comments, etc. may be displayed using word clouds on tabs. The wordclouds displayed show the most frequently occurring words or phrases inthe comments pertaining to a particular brand/product. The morefrequently a word or phrase is found in comments, the larger the word isdisplayed in the word cloud figure. In some embodiments, the frequencywith which words appear in comments may be represented with other oradditional techniques. These techniques may include changing the fonttype, emphasizing words (e.g. bold, italicize, etc.), displaying thenumber of occurrences near the word (e.g. put the number of occurrencesin parenthesis below the word), etc. The word clouds may be configuredto display information in this way with respect to a particular theme,sentiment, emotion, attribute, etc. as explained in following discussionof FIGS. 8F-8I.

FIG. 8F illustrates the word and theme cloud page. In FIG. 8F thenegative theme cloud tab is particularly displayed. The negative themecloud tab includes a negative themecloud image. This image displays themost commonly used words and phrases by size for all negative themes,words, sentiments, and/or emotions for which the comments were analyzed.The negative themecloud may be a high level image including words andphrases associated with all negative themes, words, sentiments, and/oremotions. In some embodiments, the negative themecloud image may be asummary of all or some of the negative themes, words, sentiments, and/oremotions in the comments. Also included in the negative theme cloud tabare themeclouds which illustrate the most used words and phrasesassociated with a particular theme, sentiment, and/or emotion. As isillustrated in FIG. 8F, word clouds may show the most frequently usedwords and/or phrases for particular emotions, one cloud per emotion,including outrage, anger, frustration, bitterness, disappointment,and/or confusion. In some embodiments additional themeclouds may bedisplayed for additional negative themes, words, sentiments, and/oremotions. In some embodiments, the user may customize the negativethemecloud image, for example to include or exclude certain negativeemotions (e.g. include outrage and anger but exclude confusion from thenegative themecloud image).

FIG. 8G illustrates the word and theme cloud page. In FIG. 8G thenegative word cloud tab is particularly displayed. The negative wordcloud tab includes a negative wordcloud image. This image displays themost commonly used words by size for all negative themes, words,sentiments, and/or emotions for which the comments were analyzed. Thenegative themecloud may be a high level image including words associatedwith all negative themes, words, sentiments, and/or emotions. In someembodiments, the negative wordcloud image may be a summary of all orsome of the negative themes, words, sentiments, and/or emotions in thecomments. Also included in the negative word cloud tab are wordcloudswhich illustrate the most used words associated with a particular theme,sentiment, and/or emotion. As is illustrated in FIG. 8G, word clouds mayshow the most frequently used words for particular emotion, one cloudper emotion, including outrage, anger, frustration, bitterness,disappointment, and/or confusion. In some embodiments additionalwordclouds may be displayed for additional negative themes, words,sentiments, and/or emotions. In some embodiments, the user may customizethe negative wordcloud image, for example to include or exclude certainnegative themes (e.g. include outrage and anger but exclude confusionfrom the negative wordcloud image).

FIG. 8H illustrates the word and theme cloud page. In FIG. 8H thepositive theme cloud tab is particularly displayed. The positive themecloud tab includes a positive themecloud image. This image displays themost commonly used words and phrases by size for all positive themes,words, sentiments, and/or emotions for which the comments were analyzed.The positive themecloud may be a high level image including words andphrases associated with all positive themes, words, sentiments, and/oremotions. In some embodiments, the positive themecloud image may be asummary of all or some of the positive themes, words, sentiments, and/oremotions in the comments. Also included in the positive theme cloud tabare themeclouds which illustrate the most used words and phrasesassociated with a particular theme, sentiment, and/or emotion. As isillustrated in FIG. 8H, word clouds may show the most frequently usedwords and/or phrases for particular emotions, one cloud per emotion,including excitement, cheerfulness, trust, gratitude, happiness, and/orenthusiasm. In some embodiments additional themeclouds may be displayedfor additional positive themes, words, sentiments, and/or emotions. Insome embodiments, the user may customize the positive themecloud image,for example to include or exclude certain positive emotions (e.g.include excitement and happiness but exclude trust from the positivethemecloud image).

FIG. 8I illustrates the word and theme cloud page. In FIG. 8I thepositive word cloud tab is particularly displayed. The positive wordcloud tab includes a positive wordcloud image. This image displays themost commonly used words by size for all positive themes, words,sentiments, and/or emotions for which the comments were analyzed. Thepositive themecloud may be a high level image including words associatedwith all positive themes, words, sentiments, and/or emotions. In someembodiments, the positive wordcloud image may be a summary of all orsome of the positive themes, words, sentiments, and/or emotions in thecomments. Also included in the positive word cloud tab are wordcloudswhich illustrate the most used words associated with a particular theme,sentiment, and/or emotion. As is illustrated in FIG. 8I, word clouds mayshow the most frequently used words for particular emotions, one cloudper emotion, including excitement, cheerfulness, trust, gratitude,happiness, and/or enthusiasm. In some embodiments additional wordcloudsmay be displayed for additional positive themes, words, sentiments,and/or emotions. In some embodiments, the user may customize thepositive wordcloud image, for example to include or exclude certainpositive themes (e.g. include excitement and happiness but exclude trustfrom the positive wordcloud image).

The navigation panel in the upper left hand corner of the missioncontrol and command center further includes a button to access a summarypage (summary button). In some embodiments, the summary page may includesummary information about the brand being analyzed and/or the analysisof the brand. For example, the summary page may include information suchas a theme cloud (positive) chart, the percentages of brand associatedcomments which are negative and positive, a comparison between theuser's brand and competing brands, trends for the brand (e.g. increasingor decreasing number of social media comments mentioning the brand), theintensity of positive sentiments of the brand over time. In someembodiments, the summary page may include overview or summary materialfrom one or more of the sentiment, attribute, word clouds, brand health,brand compare, roll up, and slice and dice pages. The informationdescribed on those pages as high level, overview, summary, etc. may beincorporated into the summary page.

The navigation panel in the upper left hand corner of the missioncontrol and command center further includes a button to access a roll uppage (roll up button). In some embodiments, the roll up page presents anaggregation of analysis results across multiple products of a brandand/or multiple brands selected by a user. In some embodiments, the rollup page may be used to display analysis results for a portfolio ofproducts. In one embodiment, the roll up page may display one or morebrands in hierarchical tree with attributes of a brand listed below thatbrand and sub attributes listed below the corresponding attribute.Corresponding to each entry may be analysis results at a level of detailcommensurate with the position of the brand, attribute, or sub attributein the hierarchical tree. For example, the percentage of positive andnegative emotions associated with a brand may be positioned to the rightof the brand in the tree. Below the brand may be listed one or moreproducts which fall under the brand. For each product the percentage ofpositive and negative emotions may be displayed to the right of theproduct such that a user can determine the contribution of each productto the overall emotions attached to the brand.

FIG. 8I illustrates a Mission Control and Command Center which displaysa variety of brand data to the user. In FIG. 8I the my data page isparticularly displayed. The my data page is accessed through the menuribbon and the my data button. The my data page allows a user to managethe data used in the analysis. In some embodiments, a user may takeactions such as adding data, viewing a list of all data being analyzed,removing data from the set which is analyzed, and/or otherwise managingthe data to be analyzed. In the embodiment illustrated in FIG. 8J, auser may upload data to be analyzed. The “my data” page associated withuploading data includes an information field. The information filedcontains instructions, information, and/or warnings related to uploadingdata. For example, the information field may instruct the user as to thefile type and content of the file which may be uploaded. The informationfield may also instruct the user as to the options available withrespect to uploading data as well as which information is required. Themy data page associated with uploading data includes a window with threetabs. The tabs include upload CSV file here, select fields, and success.In some embodiments, additional tabs may be included. The tabs allow theuser to navigate through the steps of the process of uploading data. Thetabs also allow for a visualization of where in the process the user iscurrently working.

The first tab is illustrated in FIG. 8J and is the upload CSV file heretab. This tab includes options to select the delimiter used in the fileto be uploaded and the file to upload. To select the delimiter used inthe file to be uploaded, a user uses a drop down menu. The drop downmenu contains a list of the available delimiters which the system mayuse. For example, the delimiter drop down menu may allow a user toselect delimiters such as commas, semicolons, periods, colons, etc. Theupload CSV file here tab of the my data page associated with uploadingdata also includes a choose file button. The choose file button allows auser to specify the file path of the file containing the data to beuploaded. In some embodiments, the choose file button may present theuser with a field to type in the file path of the file to be uploaded.In some embodiments, the choose file button may present the user with adialog box for selecting the file to upload. The dialog box may allowthe user to visually browse for the desired file. Upon selecting a file,the name of the file selected is displayed to the right of the choosefile button. If no file has been selected, no file chosen is displayed.The upload CSV file here tab further includes an upload button. When theuser actuates the upload button, the my data page associated withuploading data displays the select fields tab.

FIG. 8K illustrates the select fields tab of the my data page associatedwith uploading data. The select fields tab includes an updatedinformation field with information relevant to the select fields tab.The information filed contains instructions, information, and/orwarnings related to uploading data. For example, the information fieldassociated with the select fields tab may instruct the user as to themeaning of each field, which fields are required, which fields arerecommended, etc. The information field may also instruct the user as tothe options available with respect to uploading data as well as whichinformation is required. The select fields tab includes an input fieldlabeled study/brand name. This input field allows a user to label thedata being uploaded. The select fields tab also includes a field labeledtopics. This field displays any topics associated with the data to beuploaded. The field also allows for the user to add additional topics tobe associated with the data to be uploaded. Custom attributes associatedwith the data to be uploaded may also be selected using the menu labeledcustom attributes. Similarly one or, in some embodiments, more customidentifier columns may be selected for the data to be uploaded using thedrop down menu labeled custom identifier column. The author column dropdown menu allows the user to select an author column. The content columndrop down menu allows the user to select a content column. The datecolumn drop down menu allows a user to select a date column. In someembodiments, multiple selections may be made for one or more of thefields/dropdown menus described above. In some embodiments, the dropdown menus include options which affect how the uploaded data isdisplayed, what information is uploaded, how the information isprocessed, parameters affecting the analysis, etc. The select fields tabfurther includes a score button. The score button, allows the user tocomplete the upload of the data and to have the data analyzed by thesystem. Also included is a cancel button which allows the user to cancelthe upload and/or analysis of the data file selected in the previoustab. In some embodiments, the select fields tab may include additionalmenus for additional columns, additional attributes, additional options,etc.

The my data page associated with uploading data further includes asuccess tab. The success tab may include a filed providing an indicationto the user that the data has been successfully uploaded. The successtab may further include summary information about the data file whichwas successfully uploaded. In some embodiments, additional informationmay be provided about the data upload such as a summary of the fieldsselected, data about the file uploaded, a confirmation number, etc. Insome embodiments, the success tab may further include a button whichallows the user to upload an additional data file. In some embodiments,the success tab may further include a button which allows the user toexit the my data page and return to a home or other page. In someembodiments, the success tab may be replaced with an error tab if thedata file is not successfully uploaded. For example, if the data file isnot properly delineated the error tab may be displayed to the user. Theerror tab may include a field providing information to the user. Theinformation field may include such information as an error code, contactinformation for technical support, a diagnostic summary of the failedupload process, tips for an additional upload attempt, an estimatedcause of the error, etc. The error tab may further include a buttonwhich allows the user to attempt the upload again. This button mayreturn the user to the upload CSV file here tab or the select fieldstab. The tab the user is returned to may be tab with the selected optionwhich caused the error to occur. In some embodiments, the tabs retainthe information selected and/or input by the user. In some embodiments,the tabs and fields/menus may return to the default values. In someembodiments, the error tab may further include a button which allows theuser to cancel the upload. This button may return the user to the mydata page, home page, or another page. In some embodiments, additionalnavigation buttons may be included on each tab of the my data page.These navigation buttons may include first, previous, last, next, etc.which allow the user to navigate between tabs. In some embodiments,clicking on the tabs themselves allows navigation between the tabs.

FIG. 8L illustrates a Mission Control and Command Center which displaysa variety of brand data to the user. In FIG. 8L the my modules page isparticularly displayed. The my modules page includes a frames field. Theframes field includes a list of frames which are used by the system inthe analysis of comments related to the brand. The frames may be relatedto and/or include emotions and/or sentiments which the system identifiesin the comments associated with the brand. The frames may be theattributes, emotions, or sentiments which the system detects areassociated with the comments to be analyzed. For example, theattributes, emotions, or sentiments to be detected for association withthe brand may include happiness, enthusiasm, excitement, anger,confusion, etc. The frames are categorized in the frames field. Underthe heading default frames, the default frames are listed which thesystem uses in the analysis. Under the heading my attributes, the framesfiled lists the custom attributes defined by the user. These customattributes which are defined by the user may also be used by the systemto analyze comments associated with the brand. Within the frames fieldis an add attribute button.

The add attribute button allows the user to create an additional customattribute which may be used in the analysis. In some embodiments, theadd attribute button may also allow a user to manage (e.g. remove, edit,etc.) the custom attributes. In some embodiments, an additional buttonmay be provided in the frames field for the removal of custom or defaultframes and/or attributes. In some embodiments, the add attributes buttonprompts a user to choose from a list of attribute items to be associatedwith the new custom attribute. In some embodiments, the user may alsocreate a custom attribute item to be associated with the new attributeto be created. Attribute items are discussed in more detail withreference to FIG. 8M. By clicking on a listed frame of attribute (e.g.confusion), the user may access additional information and/or optionsrelated to that frame or attribute.

The my module page includes a detail window (labeled confusion in FIG.8L) which displays this additional information and/or options. Thedetail window may display the attribute items for a particular frame orattribute. The detail window may include other information such as adescription of the frame or attribute. In some embodiments the detailwindow may include such information as the feature, valence, position,weight, etc. of each attribute item for the particular selected frame orattribute. The detail window also includes an add attribute item. Theadd attribute item button allows the user to create an additional customattribute item which may be used in the analysis. In some embodiments,the add attribute item button may also allow a user to manage (e.g.remove, edit, etc.) the attribute items for a particular frame orattribute. In some embodiments, an additional button may be provided inthe detail window for the removal of custom or default frames and/orattributes. When a user clicks or presses the add attribute item button,a pop up field is displayed with options used to create the newattribute item.

FIG. 8M illustrates the create attribute item pop up field which isdisplayed when the user clicks or presses the add attribute item button.The pop up includes a field labeled feature. The feature field allows auser to input (e.g. type in) the feature of the new attribute item to becreated. The pop up also includes a drop down menu which allows the userto select a valence which is attached to the attribute item to becreated. For example, a user may select that the new attribute item ispositive, negative, neutral, etc. The position drop down menu allows theuser to select from options which affect the way the attribute item isused in the analysis. The user may select a variety of options relativeto the gram used in the analysis. For example, the position may bepre-gram. The pop up also includes a weight drop down menu. This menuallows the user to select a weight to give to the attribute item to becreated. The weight may be used to affect the analysis. The weight maybe selected from a variety of options. For example, the weight given aparticular attribute item may be low, lowest, medium, high, highest,etc. The pop up further includes buttons which allow the user to createthe new attribute item (create button) and to cancel the additional ofthe new attribute item and return to the my modules page (cancel). Insome embodiments, additional characteristics of the new attribute may beselected and/or modified using the create attribute item pop up.

FIG. 8N illustrates a Mission Control and Command Center which displaysa variety of brand data to the user. In FIG. 8N the my codebooks page isparticularly displayed. The my codebooks page allows a user to view,edit, and otherwise manage codebooks. In some embodiments, a codebookcontains keywords associated with a particular aspect or feature of abrand. The keywords in the codebook may be further divided intocategories based on particular sub-features or sub-aspects of the brand.For example, FIG. 8N illustrates a code book named shopping experiencewhich contains keywords relevant to a purchasers shopping experience asit relates to the brand. The shopping experience code book is furtherdivided into categories of keywords that relate to the check out counterand aisle. These categories contain individual keywords or phrasesrelevant to the category such as line or register.

The my codebooks page lists all of the codebook for a particular brandin the field labeled my code books. The my code books filed lists thecode books for a particular brand by name. To the right of each codebook are an edit button (paper and pencil) and a remove button (x). Theedit button allows a user to edit the categories and keywords which arein the codebook. The remove button removes the code book from theanalysis. Also included in the my code books field is a button whichallows a user to add one or more additional code books. The add codebook button allows a user to add an additional code book. In someembodiments, the add code book button displays an additional window tothe user. The user may use the additional window to select from alreadycreated code books relevant to various aspects of a brand (e.g. productsatisfaction). In some embodiments, the user may create a custom newcode book using the window. For example, the window may prompt a user toenter new keywords categorize them, provide example comments, etc.

The my codebooks page also includes a field which provides more detailedinformation and options regarding a code book. When a code book isselected in the my code books field, information and options associatedwith that code book are displayed in the detailed information field atthe bottom of the page. At the top of the field is a label showing whichcode book has been selected and what information is being displayed. Forexample, when the shopping experience code book is selected in the mycode books filed, the detailed information field is label shopping expand categories. The categories of keywords for that code book are thendisplayed. For example, the categories check out counter and aisle aredisplayed. The detailed information field displays the category name, asample of keywords in that category, and includes buttons which allowthe user to edit the categories and code book to which they belong. Tofacilitate editing, the detailed information field includes, for eachcategory, an edit button (paper and pencil), a remove button (x), and anexamples button. The edit button allows a user to edit the category(e.g. add or remove keywords). The remove button removes the categoryfrom the code book. The example button opens an additional view in thedetailed information field with examples of the keywords in comments.The examples button is discussed in greater detail with reference toFIG. 8O. The detailed information field also includes an add categorybutton. The add category button allows a user to add an additionalcategory to a code book. In some embodiments, the add category buttondisplays an additional window to the user. The user may use theadditional window to select from key words relevant to variouscategories common to some brands (e.g. product satisfaction keywords orcategories). In some embodiments, the user may create a custom newcategory using the window. For example, the window may prompt a user toenter new keywords, provide example comments, etc.

The examples button opens an view in the detailed information field.With reference to FIG. 8O, the examples view is added to the label atthe top of the detailed information field. In some embodiments, thislabel functions as a set of tabs that allow the user to navigate betweenviews of information for examples, specific categories, and/or codebooks. The examples view shows the user a list of example comments usingthe keywords in the category. The examples are listed under the labelexamples. FIG. 8O illustrates as example of this. The comment stuck inline is an example of the keyword line which is in the check out countercategory of the shopping experience code book. In the examples view, anedit button (paper and pencil) and a remove button (x) are located tothe right of each comment. The edit button allows a user to edit theexample (e.g. rephrase the example, correct typos, etc.). The removebutton removes the example from the category. Also included in theexamples view of the detailed information field is an add examplebutton. The add example button allows a user to add an additionalexample to a category. In some embodiments, the add example buttondisplays an additional window to the user. The user may use theadditional window to select from examples relevant to the particularcategory for which the user is viewing examples. In some embodiments,the user may create a custom new example using the window. For example,the window may prompt a user to enter a new example that uses a keywordfrom the relevant category.

In some embodiments, these examples are the examples used in the modelgeneration process described above. The addition of more examples by theuser may trigger the validation process of the model again before themodel is applies. In some embodiments, the validation step may beskipped. The addition of examples provided by the user may furtherimprove the accuracy of the model. In some embodiments, the exampleprovided by one user may be incorporated into a model that is availablefor all users of the analysis system and methods. In some embodiments, auser may be required, encouraged, or given the option to provide surveyscompleted by the authors of the additional examples to be input usingthe add example button.

FIG. 8P illustrates a Mission Control and Command Center which displaysa variety of brand data to the user. In FIG. 8P the routing rules pageis particularly displayed. The routing rules page allows a user to setrules for how data monitoring alerts will be delivered to the user.Alerts may be set up which deliver to the user information regardingchanges in the parameters monitored by the system with respect to one ormore brands. FIG. 8P illustrates the monitoring and alerting interfacefor setting up and managing the rules and alerts associated withmonitoring activities. At the top of the page is a rule summary fieldwhich includes summary information for all active monitoring rules for aparticular user. The rules are listed by name and include theinformation displayed includes the name of the rule, a description ofthe rule, the brand for which the monitoring rule applies, the actionwhich is taken when an alert rule is triggered (do this), and whetherthe rule is enabled. In some embodiments, whether the rule is enabledmay be illustrated with words (e.g. yes or no), symbols, colors, and/orany combination of the preceding. To the right of each rule are actionbuttons including an edit button (paper and pencil) and a remove button(x). The edit button allows a user to edit the rule (e.g. change ruleparameters, the brand for which the rules applies, the description ofthe rule, how the alert is delivered, etc.). In some embodiments, howthe rule alert is delivered may include e-mail, text message, etc. Theremove button removes the rule for the user. Also included in the rulesummary field is an add rule button.

The add rule button allows a user to add an additional rule. In someembodiments, the add rule button displays an additional window to theuser. The user may use the additional window to select from availablerules. In some embodiments, the user may create a custom new rule usingthe window. For example, the window may prompt a user to enter a newrule including the parameters that trigger an alert, a description ofthe rules, how the alert is delivered, etc.

The routing rules page also includes a field which provides moredetailed information and options regarding a rule. When a rule isselected in the rule summary field, information and options associatedwith that rule are displayed in the detailed information field at thebottom of the page. Each rule may be comprised of one or more ruleparameters. The rules parameters are the details of the rule and controlin what circumstances the rule will be satisfied. When the rule issatisfied, the alert is triggered. The detailed information fieldincludes the column name, operation, and value for the each ruleparameter. The column name drop down menus allow the user to select anattribute for which the rule parameter will apply. For example, the ruleparameter may look at the happiness attribute for the brand. Theoperation drop down menus allow for the user to select the operationwhich test if the rule parameter is satisfied. For example, theoperation may be whether the value of the column selected by the columnname drop down menu is equal to, less than, greater than, etc. a value.The value fields allow for the user to input a value against which thevalue of the attribute selected by the column name menu will beevaluated using the operation. In some embodiments, the value may be atotal number of comments having the selected attribute. The detailedinformation filed also includes a delete check box. If the delete checkbox is selected for a rule parameter when the update rule details buttonis selected, then the rule parameter will be removed from the rule beingupdated. The update rule button included in the detailed informationfield updates the rule with respect to the parameters changed in thedetailed information filed.

The mission control and command center further includes an inbox buttonon the menu ribbon. The inbox button takes the user to an inbox. Theinbox may store system messages to the user. For example, monitoringalerts may be sent to the indox included in the mission control andcommand center. The inbox may receive additional system updates. Forexample, the inbox may receive messages to the user informing the userof status updates regarding the analysis of data (e.g. when the analysishas begun, when the analysis is complete, etc.) The inbox may also allowthe user to communicate with support personnel such as technical supportproviders, project managers, etc.

The mission control and command center further includes a real-timebutton on the menu ribbon. The real-time button allows the user to opena window displaying real time results of the analysis techniquesdescribed herein. For example, the window displayed by pushing thereal-time button may display real time monitoring of brand trends. Insome embodiments the real-time information displayed may include realtime updates of the analysis results described herein. For example, thecharts and information viewable on other pages may be continuouslyupdated to reflect analysis which incorporates social media comments asthey are made (e.g. analysis results are updated to reflect social mediaposts on a brands social media page as they occur). In some embodiments,all or a portion of the information and display techniques describedherein may be viewed as they are updated in real time on the real-timepage.

The mission control and command center further includes a setup buttonon the menu ribbon. In some embodiments, the setup button may allow auser to configured options related to the display of the informationdescribed herein. In some embodiments, the setup button allows forcustomization of parameters that do not affect the analysis of data. Forexample, setup may include setting a user name and password, managingpermissions for others to access the information on the mission controland command center, how frequently pages are refreshed to incorporatenew data. In some embodiments, the setup button may allow a user toconfigure additional analysis parameters not otherwise customizable inother portions of the mission control and command center. Theseparameters may include such options as how frequently social mediawebsites are crawled for new comments to be analyzed, how frequently theanalysis is rerun to update information, what system resources are usedin the analysis process, allocation of processing loads and/or tasks,etc.

Additionally, the mission control and command center includes a emo-wiki(emotion wiki) button on the menu ribbon. This button allows a user toaccess the emotion wiki. In some embodiments, the emotion wiki mayinclude information pertaining to the emotions used in the analysisprocess. This may include keywords associated with certain emotions,sentiments and how they relate to the emotions, the meaning of anemotion (e.g. how an emotion is defined by the system), etc. In someembodiments, the emotion wiki may include analysis methodologyrespective to each emotion. For example, this may include informationrelevant to how emotions are detected, how false positives are reduced,how ironic comments are identified and processed, etc. An example of anemo-wiki entry is the following: Trust. Trust is an emotion thatinvolves feeling that a product is reliable, gets the job done, andhelps consumers achieve their goals. Trust is a strong indicator ofbrand loyalty, because once consumers fee that a product is worthy oftheir trust, they are highly unlikely to stray from that product.

FIG. 8Q illustrates the window which is displayed to the user when themanage brand button in the top right is selected. The manage brandswindow includes an overview field which lists the user's brands by name.For each brand listed, brand details are provided. In some embodiments,brand details include a description of the brand and/or high levelanalysis results. The default of the brand is also listed. In someembodiments, the default includes information as to whether the brand isanalyzed by default, how the brand is analyzed by default, etc. Alsoincluded to the right of each brand are action buttons including an editbutton (paper and pencil) and a remove button (x). The edit buttonallows a user to edit the brand. The remove button allows the user toremove the brand from the list of brands which are analyzed and/ormonitored. Further included in the overview field is an add brandbutton.

The add brand button allows a user to add an additional brand to thelist of brands to be analyzed and/or monitored. In some embodiments, theadd brand button displays an additional window to the user. The user mayuse the additional window to select from available brand parameters(e.g. keywords associated with the brand). In some embodiments, the usermay create a custom new brand using the window. For example, the windowmay prompt a user to enter a new brand including the parameters thatdefine the brand and/or attributes along with what values are to bemeasured.

The manage brand window also includes a field which provides moredetailed information and options regarding a brand. When a brand isselected in the overview field, information and options associated withthat brand are displayed in the detailed information field at the bottomof the page. This information includes the name of the brand and adescription of the brand. Also included are the start and end date foranalysis and/or monitoring. The start and end dates may be changed bythe user using the fields provided or the calendars. The user may alsoselect how the comments associated for the brand are analyzed using thefields to query radial buttons. The user may select analysis of the tileof comments, the content of comments, or analysis of both. The user mayalso use the provided fields to find content with particular words. Thissearching may be by all words listed, any words listed, and may excludecontent with particular words. Additionally, the field includes a topicfocus input field. The user may use the topic focus input field toselect a topic on which the analysis and/or monitoring will focus. Alsoincluded is a detect attributes drop down menu. This menu allows theuser to specify for which brand attributes the analysis and/ormonitoring will occur. FIG. 8R shows examples of options which may beselected by the detect attributes drop down menu in the manage brandswindow. Multiple attributes may be selected and may include suchattributes as particular products, sales experience, etc. With renewedreference to FIG. 8Q, the detailed information field also includes are-score button and an advanced search button. The re-score buttonallows the user to update the analysis of the brand according to theparameters selected and/or input in the detailed information field. Insome embodiments, the advance search button opens an advance searchwindow. The advance search window may allow the user to search withadditional parameters. These additional parameters may allow forsearching by particular sentiments or emotions attached to an attribute,specific characteristics of a commenter such as gender, specific mediatypes such as social media comments, etc.

Referring generally to the user interface described above, someembodiments of the user interface may use different techniques and/orcomponents to accomplish the same or similar functions. Buttons, fields,menus, links, hyperlinks, etc. may be used interchangeably wherefeasible.

The following includes a description of several emotions and other termsrelevant to the system and methods disclosed herein. The descriptionsmay, in some embodiments, be entries in the emotion wiki. In someembodiments, plug ins may be developed for each of the emotions.

Valence. Valence is a term used to describe the degree to which anemotion is perceived to be pleasant or unpleasant. An emotion is said tobe positively valenced when it is pleasant (i.e., joy, excitement), andnegatively valenced when it is unpleasant (i.e., sadness, despair).Valence is measured on a dimensional scale, meaning that it can bepositive or negative, or varying degrees between the two.

Arousal. Arousal is a term used to describe the degree to which anemotion is perceived to be activating or unactivating. An emotion issaid to have high activation when it produces feelings of energy and ofbeing “wide awake”, and to have low activation when it produces feelingsof lethargy or sleepiness. High activation is usually responsible formoving people to action, while low activation usually thwarts action.Similar to valence, arousal is measured on a dimensional scale, meaningthat it can have high activation or low activation, or varying degreesbetween the two.

Dimension. In cognitive science, affective states are measured on adimensional scale, meaning that there are varying degrees of how aparticular affective state (or emotion) can be experienced. This is animportant distinction, because at one time affective states wereconsidered to be polarities, meaning that they were either good or bad,positive or negative. Subsequently, this is how many cognitivescientists in the past have gone about measuring consumer sentiment(i.e., positive, neutral, negative). Because any given affective statecan be experienced at varying degrees, a dimensional scale ofmeasurement is much more sensitive to the complex and multifaceted humanexperience of emotion. Most affective states can be categorized alongthe dimensions of valence and arousal (see above).

Cognitive Affect States. Some emotions are classified ascognitive-affect states, rather than pure affect states.Cognitive-affect states constitute a blend of cognitive and affectiveprocesses, where affective experiences are mediated by cognitiveprocesses like reasoning, deliberation, or comparing desired outcomes toactual outcomes.

Emotions. Emotions are generally conceptualized as multifaceted,embodied phenomena that involve loosely coupled changes in the domainsof subjective experience, behavior, and peripheral physiology. Emotionis associated with mood, temperament, personality, disposition, andmotivation. Motivations direct and energize behavior, while emotionsprovide the affective component to motivation, positive or negative.

Consumer-based emotions. Years of theoretical and empirical work havebeen devoted to understanding emotions as they occur in everyday living.However, we suspect that the emotions that arise during consumer/productinteractions are unique unto themselves. The goal of the system andmethod herein is to understand how emotions like anger, confusion,frustration, etc. are unique when they involve consumers' relationshipswith brands, products, or services. Thus, the system and method hereindefines our emotions as “consumer-based emotions”. We hypothesize (andhave data to support) that generic frustration is qualitativelydifferent from consumer-based frustration. Whereas frustration in everyday life might involve feelings of goals being blocked, consumer-basedfrustration appears to be specifically related to the negative feelingsassociated with spending money on a product that does not do what it isintended to do, and thus prevents consumers' goals from being achieved.For this reason, many of emotions, used in the system and method herein,(as listed below) different somewhat from their standard and genericdefinition.

Happiness. Happiness is an emotion of well-being classified as havingpositive valence and high activation, and is characterized by positiveemotions ranging from contentment to intense joy. Happiness is a fuzzyconcept and can mean many things to many people. Part of the challengeof a science of happiness is to identify different concepts ofhappiness, and where applicable, split them into their components.Happiness may be a more stable trait (meaning that people either have apredisposition toward being happy or unhappy), rather than a transitorystate (see cheerfulness and enthrallment below).

Cheerfulness. Cheerfulness is a positive emotion that is typicallycharacterized by feelings of optimism and positivity. It is a transitoryemotion that tends to arise in response to an event or situation, anddecay when the event or situation is over.

Enthrallment. Enthrallment is a transitory, positive emotion thatinvolves being “spellbound” or “captivated”. People tend to describe thefirst moments of owning a new product as being enthralling, because allattention resources are directed toward learning about/using theproduct.

Excitement. Excitement is a positive emotion that is highly similar tocheerfulness. Excitement can be differentiated from cheerfulness becauseexcitement is a high intensity emotion, whereas cheerfulness is aneutral intensity emotion.

Disappointment. Disappointment is a cognitive-affective state thatresults when a person compares desired outcomes to actual outcomes, andhas a sense of being “let down”. For example, a person who purchases a4G Phone with the hope that it will be significantly faster than the 3GPhone, and discovers that it is only marginally faster, will experiencedisappointment.

Frustration. Frustration is a negative, activating cognitive-affectivestate that results when a person perceives that his or her goals arebeing blocked. In the context of learning, for example, frustrationarises when students needs to learn about how the endocrine system andcirculatory system work together to promote the healthy functioning ofthe body, but perceive that the textbook they are using isincomprehensible or written above their grade level. Individuals can befrustrated because of external blockages to their goals (i.e., a poorlywritten textbook), or because of internal blockages (i.e., poor readingskills). In the context of consumer behavior, people might becomefrustrated if they purchase a data analysis tool to help them betteranalyze data, but cannot use the tool because it does not work withtheir operating system.

Irritation. Irritation is a somewhat negative, low activating emotionthat usually precedes frustration. It is characterized by feelings ofagitation or “grouchiness.”

Confusion. Confusion is a cognitive-affective state that results whenpeople perceive a mismatch between their expectations and actualoutcomes. Like the example above with the 4G Phone, people mightexperience disappointment when they perceive that the 4G is simply notas fast as advertised. However, people might experience confusion ifthey perceive that there is some other explanation for why the 4G is notas fast as expected (i.e., “perhaps something's wrong with this one”, or“maybe I didn't set something up properly”).

Contempt. Contempt is an intensely negative emotion that involvesregarding an object or person as inferior, base, or worthless—it issimilar to scorn. Contempt is involves having an open hatred ordisrespect for on object or person, and usually arises when a personperceives that they have been INTENTIONALLY harmed, deceived, ormistreated by someone else.

Remorse. Remorse is a negative emotion that usually involves self-blame,guilt, or regret. A good way to think about remorse is“Disappointment+guilt/self-blame”. For example, if a person spends$1,000 on a new laptop and realizes that she dislikes the operatingsystem, she will probably feel disappointed in the product, and willblame herself for spending a large sum of money on a product that shecan't use. In terms of the linguistic analyses we've conducted so far,remorseful people usually talk about “wasted money”.

Pride. Having a sense of pride is a positive affective state thatinvolves regarding one's self, one's achievements, or one's possessionsas having a high sense of worth to one's self and/or to others.

Anger. Anger is an intensely negative, highly activating emotion thatarises when people perceive that they have been ill-treated, treatedunfairly, or been deceived. It is a feedback mechanism in which anunpleasant stimulus is met with an unpleasant response.

Outrage. Outrage is an intensely negative, highly activating emotionthat is usually preceded by anger. In fact, it perhaps makes sense toconsider outrage as “out of control anger”.

Wow. Wow is a state of shock that is best described as being “pleasantlysurprised”. It involves feeling that your expectations have beenexceeded, the product/service is better than anything else on themarket, and is accompanied by a lot of -er words (it was better,stronger, softer, cheaper, etc). It is usually correlated with purchaseintent.

Boo. “Boo” is conceptualized as the opposite of “wow”. That is, itoccurs when a person is unpleasantly surprised. Boo is an emotion thatis highly similar to disappointment, and is sometimes difficult todifferentiate from disappointment. The key difference between the twoemotions is that disappointment involves a comparison of expectations tooutcomes, while boo may not. Rather, people who experience boo might nothave had any expectations at all. A person who experiences boo might belikely to say something like, “I didn't know what to expect when I triedit, but I really didn't like how it worked”.

Mental Model Change (MMC). MMC involves more than just feelingpleasantly surprised. It's a feeling that your life is somehow improvedbecause the product exists. Whereas Wow can be somewhat fleeting (WOW .. . that bleach pen really removes stains well!), MMC is long pastingand is associated with purchase intent AND some kind of behavioralchange (Now that I know that this bleach pen gets rid of stains, I canplay outside with my children more often!”)

Gratitude. Consumers experience gratitude when they feel that a productor service has finally met their expectations or needs. Whereas “wow”occurs when expectations have been exceeded, gratitude tends to arisewhen expectations are simply met. Gratitude often occurs after a stintof disappointment or irritation, when a new product (or perhaps animproved version of the same product) finally does what the product is“supposed to do”. Gratitude is also likely to arise when the consumerperceives that the manufacturer of a product cares about their needs orexpectations.

Trust. Trust is an emotion that involves feeling that a product isreliable, gets the job done, and helps consumers achieve their goals.Trust is a strong indicator of brand loyalty, because once consumersfeel that a product is worthy of their trust, they are highly unlikelyto stray from that product.

Bitterness. Bitterness is a deep-seated feeling of ill will that resultsfrom a negative consumer experience. Consumers who feel bitter toward amanufacturer, brand, or company tend to hold on to the equivalent of “agrudge” against that manufacturer/brand/company, even when themanufacturer/brand/company makes efforts to rectify the problem.

Referring now to FIG. 9, a flowchart of a process 900 for identifying anemotion, a cognitive state, a sentiment, or other attribute associatedwith a document is shown, according to an exemplary embodiment. Process900 may be performed by one or more components of the systems describedwith reference to FIGS. 1-8R. In some embodiments, process 900 may beused to determine whether a document (e.g., a webpage, a HTML document,a word processing document, a portable document format (PDF) document,etc.) expresses or implies a particular emotion (e.g., happiness,confusion, frustration, etc.) or sentiment (e.g., loyalty, value, trust,etc.). In various embodiments, process 900 may be used to determine aparticular cognitive state (e.g., cognition, deception, intent, etc.)possessed by an author of a document or to identify an attribute or typeof a document (e.g., promotional, educational, etc.). Process 900 may beperformed to determine the sentiment that an author of a document hastoward a particular topic or brand.

Process 900 is shown to include selecting a focus word or phrase in adocument (step 902). The focus word or phrase may be, for example, abrand or topic about which a customer has expressed a sentiment the textof a document. The text of the document may be analyzed in a windowaround the focus word/phrase. The window may define a range of words oneither side of the focus word/phrase in which the analysis is performed.The window size can be adjusted and the text of the document can beanalyzed for various window sizes around the focus word/phrase. In someembodiments, the entire text of a document is analyzed.

Process 900 is shown to include determining a word count (“wc”) of theanalyzed text (step 904). The word count may be the total number ofwords in the document (e.g., if the entire document is analyzed) or anumber of words in a window around the focus word/phrase. The analyzedtext is parsed to determine a number of positive words or phrases(“posGWc”) present in the analyzed text (step 906) and a number ofnegative words or phrases (“negGWc”) present in the analyzed text (step908). A positive word count ratio (“posWCR”) is determined by dividingthe number of positive words or phrases by the word count (i.e.,posWCR=posGWc/wc) (step 910). A negative word count ratio (“negWCR”) isdetermined by dividing the number of negative words or phrases by theword count (i.e., negWCR=negGWc/wc) (step 912).

Still referring to FIG. 9, process 900 is shown to include calculating avalance distance (“valanceDistance”) by taking the absolute value of thedifference between the positive word count ratio and the negative wordcount ratio (i.e., valanceDistance=abs(posWCR-negWCR) (step 914). Thevalance distance may indicate whether the sentiment or emotion of thedocument is highly positive or negative, moderately positive ornegative, or fairly neutral.

Process 900 is shown to include defining a document valance confidencevariable based on the calculated valance distance (step 916). Step 916may include comparing the calculate valance distance with variousthreshold values. If the valance distance is less than a low thresholdvalue (e.g., valanceDistance<0.20), step 916 may include setting thedocument valance confidence variable to low. If the valance distance isbetween the low threshold value and a high threshold value (e.g.,0.20<valanceDistance<0.60), step 916 may include setting the valanceconfidence variable to medium. If the valance distance is greater thanthe high threshold value (e.g., valance distance>0.60), step 916 mayinclude setting the document valance confidence variable to high.

Still referring to the FIG. 9, process 900 is shown to include comparingnumber of positive words or phrases in the document with the number ofnegative words or phrases in the document (step 918). If the number ofpositive words or phrases is the same or substantially the same as thenumber of negative words or phrases (i.e., posGWc=negGWc), process 900may include setting a primary document emotion variable to neutral (step920).

If the number of positive words or phrases exceeds the number ofnegative words or phrases (i.e., posGWc>negGWc), process 900 may includesetting the primary document emotion variable to the emotion associatedwith the highest positive frame count (e.g., Crave>Happiness>Gratitude)(step 922). The frame count associated with an emotion may be the numberof words or phrases in the document associated with the emotion. Severallists of words or phrases associated with various emotions are providedbelow. Process 900 may further include setting the primary documentemotion score variable to the positive frame count (step 924).

If the number of positive words or phrases is less than the number ofnegative words or phrases (i.e., posGWc<negGWc) process 900 may includesetting the primary document emotion variable to the emotion associatedwith the highest negative frame count (e.g.,Anger>Frustration>Disappointment>Confusion>Not_Happy>Not_Grateful) (step926). Process 900 may further include setting the document primaryemotion score variable to the negative frame count (step 928).

Several examples of attribute, emotion, and cognitive state frames arelisted below. Each list includes a heading identifying an emotion,sentiment, cognitive state, or attribute. The items within each list areexemplary words or phrases (i.e., language features) that increase theframe count of the associated heading if the word or phrase is foundwithin the examined text. The items within each list can be interpretedas the language of the emotion, sentiment, cognitive state, or attributeidentified by the heading.

In some embodiments, list items are provided with a prefix of CEN, PRE,or POS. These indicate the position (Prefix, Center, or Post,respectively) that the language feature can be found in relation to thefocus word or phrase.

In an exemplary embodiment, documents or portions of documents can betagged by more than one attribute or emotion. Many documents or portionsof documents or users can be evaluated. Predictive algorithms mayprocess numerical tallies and/or scores to predict the future emotionassociated with a product, product release, brand name or other metric.

Example of PROMOTIONAL Attribute Frame:

-   CEN|best buy-   CEN|bestbuy-   CEN|black friday-   CEN|blackfriday-   CEN|buy one get one free-   CEN|christmas deals-   CEN|contest rules-   CEN|cyber monday-   CEN|cybermonday-   CEN|daily deals-   CEN|dailydelas-   POS|do you have-   PRE|factory unlocked-   PRE|for sale-   CEN|half off your next purchase-   CEN|here's your chance to get one-   PRE|in original box-   PRE|in original packaging-   CEN|last minute deals-   CEN|looking for deals-   CEN|lowest prices-   PRE|new in box-   CEN|one stop shop-   CEN|replacement for-   PRE|sealed with receipt-   CEN|small through-   POS|selling my-   CEN|how to unlock-   CEN|enter to win-   CEN|if you tweet:-   PRE|will send you-   CEN|holiday season is coming

Example of Emotion ‘Happiness’ Frame:

-   CEN|will help-   CEN|with joy-   CEN|wonderful-   CEN|wonderful i-   CEN|worked great-   CEN|works great-   CEN|worth the money-   CEN|you will be loved forever-   PRE|I loved it-   PRE|a great product-   PRE|all the way-   PRE|always cheers me up-   PRE|always makes my day-   PRE|and it is amazing-   PRE|and love it-   PRE|are the best-   PRE|awesome-   POS|enjoyed a-   POS|enjoying a-   POS|enjoying my-   POS|excited to try-   POS|exciting to have-   POS|getting-   POS|go with

Example of Cognitive State ‘Connection’ Frame:

-   PRE|allows me to-   CEN|community of-   PRE|gives me a sense of belonging-   PRE|makes me feel a sense of belonging-   CEN|my sense of belonging-   CEN|one of the team-   CEN|part of the group

Models that can be used with and benefit from the above frame basedprocessing and document scoring:

-   Happiness-   Confusion-   Frustration-   Bitterness-   Cheerfulness-   Excitement-   Enthusiasm-   Gratitude-   Trust-   Contempt-   Remorse-   Irritation-   Disappointment-   Anger-   Outrage-   Embarrassment-   Behavior Shift-   Dynamic Themes-   Boo-   Not Happy-   Not Grateful-   Surprise/Shock-   Crave/Desire-   Unmet Needs-   Met Needs-   Value

Category Level Models:

-   CPG Category Models-   Vehicles-   Telecommunication-   Promotion-   Availability

Individual Difference Models

-   Personality (Big 5)-   Gender-   Age-   Ethnicity-   Education

Additional Models

-   Refined Influence-   Purchase Path-   Churn-   Launch Monitor-   Dynamic Share of Voice/Brand Awareness-   Brand Equity (Brand Performance Index)-   Loyalty (Satisfaction, Purchase Intent, Recommend, Return)-   Cognition (Attitudes, Perceptions, Beliefs, Motivators)-   Deception (Sarcasm, Irony, False Positives, False Negatives)-   Behavioral Economic Models-   New Category Models (Entertainment, Travel & Lodging, Food and    Beverage, Media, High Tech, etc.)-   Consumer Engagement Index (By Category)

Referring now to FIGS. 10-15, several processes 1000-1500 for rule-basedconstruct detection and scoring are shown, according to an exemplaryembodiment. Processes 1000-1500 may be performed by one or morecomponents of the systems described with reference to FIGS. 1-8R.Processes 1000-1500 may be used to assign various scores to a documentbased on textual data contained therein. Each score is associated with“construct.” Constructs may include, for example, sentiments, emotions,opinions, recommendations, cognitive states, or other qualitativeattributes or themes expressed in a document. Some exemplary constructsinclude happiness, confusion, frustration, bitterness, cheerfulness,excitement, enthusiasm, gratitude, trust, thoughtfulness, impactfulness,contempt, remorse, irritation, disappointment, anger, outrage,embarrassment, behavior shift, dynamic themes, boo, not happy, notgrateful, surprise, shock, crave, desire, unmet needs, met needs, value,and/or other sentiments or mental states that can be expressed in adocument.

Processes 1000-1500 may be used to identify one or more constructs in adocument and to assign a score to the identified constructs based on thetextual data of the document. The score for each construct may be at thedocument level. In other words, each document may have adocument-specific score for each of the identified constructs. Documentsmay include, for example, consumer reviews, articles, essays, socialmedia posts, user comments, or other forms of textual data. For example,the systems and methods of the present disclosure may be used to detectand score various sentiments expressed in consumer reviews of productsor services. The score assigned to each construct may be intuitive(e.g., easily interpretable) and suitable for a wide range ofmathematical and statistical analyses. For example, the score for aconstruct may operate as a continuous variable for statistical analyses.

In some embodiments, each of the identified constructs has a positiveand negative form. The systems and methods described herein may balancepositive evidence of a construct with negative evidence of the constructto assign a construct-specific score to a document. The evidence may betextual data extracted from the document. Each construct may be assigneda scaled score. In some embodiments, the assigned scores range from 0 to10 where 0 is the minimum score, 5 is an unclassified score, and 10 isthe maximum score. Any score between 0 and 4 may be deemed negative, andany score between 6 and 10 may be deemed positive.

In some embodiments, the systems and methods described hereinasymmetrically guard against false positive results. Specifically,minimum or maximum scores (e.g., scores of either 0 or 10) may have thefewest instances of false positives. Scores between the minimum andmaximum may have descending priority of detecting false positives basedon the difference between the score and the closest minimum or maximumFor example, scores of 9 and 1 may have the next highest priority,followed by scores of 8 and 2, and so forth. In this way, scores of 5(unclassified) may have the lowest priority for detecting falsepositives.

Balancing the positive evidence of a construct with the negativeevidence of a construct is made possible by the identification ofrelevant “grams” and “features” in the document. As used herein, a gramis a string of textual characters (e.g., letters, numbers, symbols,special characters, etc.) for which the meaning of the text string isnot the focus. Grams represent keywords, phrases, or other text stringswhich can be counted in a document. The number of instances of variousgrams in a document may indicate whether the overall nature of thedocument is positive or negative and can be used to identify and scorevarious constructs.

In contrast, a feature is a string of textual characters for which themeaning of the text string is the focus. Features may be patterns ofwording that indicate a positive signal or a negative signal. Forexample, the pattern of wording “I recommend this product” is a featurebecause it provides a meaningful indication that the reviewer likes theproduct and thinks others should use it. Features may include one ormore grams. For example, positive features may include such grams as “Ilove it,” “I recommend it,” or “it was easy to use.” Negative featuresmay include such grams as “I hated it,” “it broke,” or “I'd never buyit.” Throughout this document, the terms “gram” and “feature” may beused interchangeably.

In some embodiments, various features may be classified as members of a“driver.” A driver may be a general category such as “positivesentiment” or “recommendations.” Various features may belong to one ormore drivers. For example, the feature “I love it” may be a member ofthe “positive sentiment” driver, whereas the feature “I recommend it”may be a member of the “recommendations” driver.

Grams may fall into several categories of evidential confidence. Thecategories currently employed in the architecture include “Excellent,”“Good,” “Fair,” “Questionable,” “Bad,” and “Terrible.” In someembodiments, the categories further include “Prototypical Positive” and“Prototypical Negative.” The names of these categories reflect thedegree to which the member grams are likely to be influential indetermining the positive value of the construct. For example, Excellentgrams are important evidence of a positive construct; whereas Terriblegrams are equally important counter-evidence. The categories and themethod by which grams are derived and populated is described in greaterdetail with reference to FIG. 10.

The scaled score for a construct for any given document may be derivedfrom a rule-based architecture which considers positive and negativeevidence of the construct under investigation. For example, the text ofthe document may be analyzed to identify occurrences of various gramsand features. Grams and features may provide positive and negativeevidence of a construct based on the classification of the grams andfeatures with respect to the construct (e.g., whether the gram is“excellent” evidence of the construct or “terrible” evidence of theconstruct). The methodology for assigning scores to constructs isdescribed in greater detail with reference to FIGS. 13-15.

Referring now to FIG. 10, a flowchart of a process 1000 for derivinggrams and categories is shown, according to an exemplary embodiment.Process 1000 may be performed by one or more components of the systemsdescribed with reference to FIGS. 1-8R. Process 1000 is shown to includeclassifying each of a set of multiple documents as either positive ornegative with respect to a construct (step 1002). Step 1002 may includeusing a gateway metric to approximate the polarity of the documents.Polarizing the set of documents with respect to a particular constructmay facilitate identification of various grams relevant to theconstruct. For example, grams that provide evidence of a positive formof the construct may occur more frequently in the documents classifiedas positive with respect to the construct, whereas grams that provideevidence of a negative form of the construct may occur more frequentlyin the documents classified as negative with respect to the construct.Step 1002 may produce two sub-datasets (i.e., a positive baseline and anegative baseline). Each data set may include one or more documents.

Still referring to FIG. 10, process 1000 is shown to include comparingthe documents classified as positive with the documents classified asnegative (step 1004). Step 1004 may include performing a statisticalanalysis on the documents to determine whether any particular grams(i.e., text strings) are correlated with the classification. Forexample, some grams may occur more frequently in documents classified aspositive or negative with respect to the construct. Step 1004 mayinclude identifying a number of occurrences of each identified gram ineach document.

Still referring to FIG. 10, process 1000 is shown to include extractinggrams from the documents and assigning scores to the grams based on thecomparison (step 1006). Step 1006 may include determining whether any ofthe identified grams occur more frequently in the positive classifieddocuments or the negative classified documents. In some embodiments,step 1006 includes identifying a ratio between a number or rate ofoccurrences of a particular gram in the positive documents and thenumber or rate of occurrences of the gram in the negative documents.Scores may be assigned to each gram based on the identified occurrenceratio. For example, grams that occur more frequently in the positivedocuments may be assigned a relatively higher score, whereas grams thatoccur more frequently in the negative documents may be assigned arelatively lower score.

Step 1006 may produce an extracted list of grams, derived from thedocuments, with each gram having a positive or a negative value. Theextracted grams may include many grams that are potentially relevant tothe identification of the construct at issue. In some embodiments, thelist of extracted grams and the scores associated therewith define amodel for scoring various documents. For example, highly-scored gramscan be used as indicators of the construct at issue in another document.In other embodiments, the list of extracted grams and scores are used todevelop a more sophisticated model (described in greater detail withreference to FIGS. 12-15).

Still referring to FIG. 10, process 1000 is shown to include assigningeach extracted gram to a category based on a degree to which the gramevidences the construct (step 1008). Categories may include, forexample, Excellent, Good, Fair, Questionable, Bad, Terrible,Prototypical Positive, Prototypical Negative, and Other. In someembodiments, grams are assigned to categories based on the scoresassigned to the grams in step 1006. For example, grams with the highestscores may be assigned to the Excellent category, whereas grams with thelowest scores may be assigned to the Terrible category.

A gram may be assigned to the Excellent category if the gram is anexcellent example of the construct under consideration. For example, thegrams “I love it” or “I recommend it” may indicate language thatdescribes a highly positive product experience and may be assigned tothe Excellent category.

A gram may be assigned to the Good category if the gram indicates arelatively good product experience without necessarily rising to theExcellent level of the construct under consideration. For example, thegrams “I liked it” or “works effectively” may indicate language thatwould describe a positive product experience and may be assigned to theGood category. Although grams assigned to the Good category may not seemultimately important to the constructs of investigation, it is notedthat many product experiences are a relationship between outstandingfeatures and merely good features. This relationship is the subject ofthe scoring methodology described in greater detail with reference toFIGS. 13-15.

A gram may be assigned to the Fair category if the gram indicates a fairproduct experience, which does not rise to the Good level of theconstruct under consideration. For example, the grams “it was OK” or“acceptable” may indicate language that would describe a low positiveproduct experience and may be assigned to the Fair category. Like Goodgrams, Fair grams may not seem ultimately important to the constructs ofinvestigation. However, because many product experiences are arelationship between various levels of appraisal, Fair grams arerelevant to the scoring methodology.

A gram may be assigned to the Prototypical Positive category if the gramis used in a document describing a generally positive product experiencewithout necessarily having overtly positive words. For example, the gram“hey guys and girls” and “My family will be” are examples of thelanguage people often use when they are being overtly positive elsewherein the text. While such an assumption of positive language will be rightfar more often than it is wrong, it is still prone to error. As such,this category of grams may have the lowest priority is the scoringarchitecture and/or may be omitted entirely in some embodiments.

Although only the positive examples of gram categories (e.g., Excellent,Good, Fair, and Prototypical Positive) are explained here in detail,their negative counterparts may also exist within the architecture(e.g., Terrible, Bad, and Prototypical Negative). The description of theTerrible category is the opposite of the description of the Excellentcategory. The description of the Bad category is the opposite of thedescription of the Good category. The description of the PrototypicalNegative category is the opposite of the description of the PrototypicalPositive category.

A gram may be assigned to the Questionable category if the gramindicates the existence of positive or negative language withoutexplicitly containing positive or negative language. An example of aQuestionable gram is the word “however.” This word is neither positivenor negative (in terms of polarity), and yet its presence suggests thatboth positive sentiment and negative sentiment may exist in the document(e.g., on either side of the Questionable gram). Other examples ofQuestionable grams may include “although,” “but,” “still,”“nonetheless,” “nevertheless,” “even though,” “conversely,” “on theother hand,” or other words of phrases that indicate the presence ofboth positive and negative sentiments.

Questionable grams may be extremely useful in the architecture for tworeasons. First, Questionable grams are effective for guarding againstfalse positives (and false negatives). For example, tracking everypossible kind of positive and negative language may be extremelydifficult. However, it's an easier task to collect a very large numberof Questionable grams. If a positive or negative gram is missed, thepresence of a Questionable gram may provide a warning that the text isnot completely positive or completely negative and may trigger thereduction of a high confidence score associated with a document.

The second use of Questionable grams relates to the detection andscoring of certain types of constructs. For example, Questionable gramsmay indicate that the document is more detailed, considered, reflective,organized, crafted, reasoned, contemplative, or demonstrative of deeperlevels of cognitive processing. Such attributes may be characteristic ofa “thoughtfulness metric” construct expressed in the document. Thethoughtfulness metric construct is described in greater detail withreference to FIG. 14.

Still referring to FIG. 10, process 1000 is shown to include expandingand validating the extracted grams (step 1010). Expanding extractedgrams may include identifying numerous grams with meanings similar tothe grams identified and extracted in step 1006. For example, if thegram “pleased” is extracted from the set of documents, the gram“thrilled” may be generated in step 1010 because the two grams have asimilar meaning. Although these new grams may not have occurred in theinitial dataset analysis, it is possible that such grams may exist infuture documents.

In some embodiments, step 1010 includes generating grams based on commonmisspellings. Because misspellings are common in text such as consumerreviews or social media posts, it may be useful to implement a procedurefor handling these misspellings. In some embodiments, step 1010 includessimply correcting the spellings. However, this process can be timeconsuming, computationally expensive, and runs the risk of introducingnew errors. Correcting misspellings also assumes the misspelling isunintentional and robs the data of potential characteristics of theperson who write the text.

In some embodiments, step 1010 includes identifying whether themisspelling is a mistake or an error. A mistake is an unintentionalmisspelling, whereby the writer would have written the correct spellinghad there been the opportunity. By contrast, an error is the intentionalspelling of a word that does not conform to most dictionaries. Forexample, the verb “recommend” is commonly misspelled as “reccommend” andthe two-word adverbial “a lot” is commonly spelled as one word “alot.”In some embodiments, expanding the extracted grams includes retainingcommon misspellings and adding the correct spellings.

In some embodiments, step 1010 includes dropping apostrophes. Whenever agram with an apostrophe occurs, the non-apostrophe version may also beadded. For example, if the gram “I wouldn't recommend it” wasidentified, then the gram “I wouldnt recommend it” may also be added.

In some embodiments, step 1010 includes adjusting for contractionnegations. There are numerous ways to express a negation in English. Oneof the most common forms is to use the word “not.” However, the word“not” is often abbreviated to “n't.” Because these forms are both verycommon, all abbreviated and full versions of extracted grams may beincluded in the set of grams. For example, if the gram “I wouldn'trecommend it” was identified, then the gram “I would not recommend it”may also be added. Similarly, if the gram “I would not recommend it” wasidentified, then the gram “I wouldn't recommend it” may also be added.

In some embodiments, step 1010 includes modifying grams for optimalperformance (e.g., to improve recall/match rates in subsequent text).Most grams that start with the word “I” can have that word deleted. Thisdeletion allows for greater recall rates (e.g., instances of occurrencein other documents) without affecting accuracy. For example, the gram “Iwill buy it” can become simply “will buy it.” This deletion allows formany new instances to be included such as “definitely will buy it” whichwould not match the version of the gram with the personal pronoun.

In some embodiments, step 1010 includes adding negative versions ofgrams. For instance, if a gram such as “a great product” was identifiedand extracted from the set of documents, then the gram “not a greatproduct” may also be added. In some embodiments, if a text string in thedocuments matches two or more grams, the longest gram may be consideredas the best match for the text string. This helps to identify grams withthe prefix “not” in front of an otherwise positive sentiment. (e.g.,“not a great product”).

In some embodiments, step 1010 includes validating the extracted grams.One technique for validating the extracted grams is face validation.Face validation is a broad approach for authenticating that any givenappraisal is what it is supposed to be. As the name suggests, facevalidation primarily involves looking at the data and its associatedoutput and checking (on the face of it) whether the result makes sense.Although face validation may not be the final justification of asystem's performance, face validity may be a threshold test before morerigorous validation methods are employed.

Referring now to FIG. 11 a flowchart of a process 1100 for validatinggrams is shown, according to an exemplary embodiment. Process 1100 maybe performed by one or more components of the systems described withreference to FIGS. 1-8R. In some embodiments, process 1100 may be usedto accomplish the gram validation component of step 1010, described withreference to FIG. 10.

Process 1100 is shown to include assessing texts assigned a maximumscore or a minimum score for accuracy (step 1102). Step 1102 may includeexamining documents which receive a score of 10 (maximum) and 0(minimum) to determine whether the document is actually extremelypositive or extremely negative with respect to the construct at issue.Documents that are assigned a maximum score or a minimum score may bethe highest priority for validation due to the increased importance ofavoiding false positives and false negatives for documents at anextremum of the score range.

Still referring to FIG. 11, process 1100 is shown to include identifyingnew grams for correction in response to a determination that the scoresassigned to at least a threshold value of the assessed texts areerroneous (step 1104). For example, if more than 5% (or any otherpercentage, proportion, or quantity) of the texts are assignedinaccurate scores, then new grams may be identified for correction. Theidentification performed in step 1104 may also lead to tweaks in thearchitecture (e.g., adding additional categories, adjusting the scoringmethodology, etc.).

Still referring to FIG. 11, process 1100 is shown to include assessingfor accuracy texts assigned a score incrementally less than the higherscore of the previously assessed texts or incrementally more than thelower score of the previously assessed texts (step 1106). Step 1106 maybe performed in response to a determination that fewer than thethreshold amount of texts assessed in step 1102 are assigned inaccuratescores. Step 1106 may initially include checking for errors in the textsassigned scores of 1 and 9. The score 1 is incrementally more than theminimum score of 0 and the score 9 is incrementally less than themaximum score of 10. In this embodiment, the increment is an integervalue (i.e., 1).

If the error rate of the assessed texts is greater than the thresholdvalue, process 1100 is shown to include repeating step 1104 to identifynew grams for correction. The grams may be updated until the error ratefor the assessed texts is less than the threshold value. Once the errorrate for the assessed texts is less than the threshold value, step 1106may be repeated. After assessing the texts assigned scores of 1 and 9,the texts assigned scores of 2 and 8 are assessed. The score 2 isincrementally more than the previous assessed score of 1 and the score 8is incrementally less than the previous assessed score of 9. As shown inFIG. 11, steps 1104 and 1106 may be repeated until the scoring errorrequirements for all of the classified texts are satisfied.

Still referring to FIG. 11, process 1100 is shown to include unpackingunclassified texts (step 1108). In the scoring range from 0 to 10, textsassigned a score of 5 may be unclassified or neutral (i.e., neitherpositive nor negative with respect to a particular construct). However,many unclassified texts are, in reality, either positive or negative. Insome embodiments, text may be unclassified if none of the categoriescontained member grams that were present in the text. If no member gramsare present, the document may receive no hits and may be unclassified(e.g., assigned a score of 5).

Texts may have no identified categories for one of several reasons.First, the text may have no identified categories if the text simply hasno positive or negative elements. Such a text can be described as a“natural unclassified.” Second, a text may have no identified categoriesif the category lists do not yet include a reasonable gram that ispresent in the document text. In step 1104, new grams can be identifiedto correct for such scoring errors. Such a text can be described as“under-specified.” Third, a text may have no identified categories ifthe identified grams are over specific. For example, the phrase “smoothreusable product” is clearly a positive attribute; however, such aphrase is highly specific and may not match any of the identified grams.Such a text can be described as “over-specified.” Fourth, a text mayhave no identified categories if the Prototypical Positive grams andPrototypical Negative grams have not been added to the architecture. Forexample, grams such as “hey guys and girls” and “My family will be” (andnumerous others) are not, in and of themselves, positive or negative;however, they do tend to co-occur in positive and negative text. Such atext may be described as “potentially specified.”

Other texts may be unclassified because too many of the categoriescontained member grams that were present in the text (i.e., the text hashigh evidence of both positive and negative information). If too manymember grams are present, the document may receive a high-multiple hitrate (or driver activation) that is currently outside the range of thearchitecture. Such documents may also be unclassified and assigned aneutral score (e.g., assigned a score of 5).

Texts that have too many identified grams across categories may falloutside of the current architectural assignment rules. An example ofsuch a text is one that has multiple hits for the Excellent category andmultiple hits for the Terrible category. When texts are relatively long,they may include multiple pieces of contrasting evidence. Such texts maybe rich in information, and may be good examples of “thoughtfulnessmetric” texts (described in greater detail with reference to FIG. 14).

In some embodiments, step 1108 includes classifying unclassified textsbased on the text's “rightedness.” The polarity of a text with multiplecontrasting category activations (positive or negative) is most likelyto be evidenced by the final identified gram of the text. For example,if the final identified gram in the text is from the Excellent category,then the text is likely to be positive. By contrast, if the finalidentified gram of a text is from the Terrible category, then the textis more likely to be negative. Step 1108 may include determining whetherthe final identified gram is positive or negative and adjusting thescore of the unclassified text accordingly. In step 1108, the scoreassigned to a document can be adjusted so any document initially scoredas neutral (e.g., assigned a score of 5) incremented if the final gramis a positive gram (e.g., changed from a 5 to a 6) or decremented if thefinal gram is a negative gram (e.g., changed from a 5 to a 4).

In some embodiments, step 1108 includes classifying texts based on therelationship between the number of positive themes and negative themesin the text. For example, if the ratio of positive to negative themes ishigher than a threshold value in all identified positive texts, then agiven unclassified document with a similar ratio is likely to also bepositive. The same supposition as ratio scoring can be applied todensity scoring or difference scoring.

In some embodiments, step 1108 includes determining the ratio, density,or difference between positive grams and negative grams in a collectionof documents scored positively and in a collection of documents scorednegatively. If the ratio, density, or difference between the positiveand negative grams in the text currently being scored is closer to theratio, density, or difference associated with the positive documents,the text currently being scored may be assigned a positive score.Conversely, if the ratio, density, or difference between the positiveand negative grams in the text currently being scored is closer to theratio, density, or difference associated with the negative documents,the text currently being scored may be assigned a negative score.

Referring now to FIG. 12, a flowchart of a process 1200 for definingdrivers is shown, according to an exemplary embodiment. Process 1200 maybe performed by one or more components of the systems described withreference to FIGS. 1-8R. A driver may be a general category such as“positive sentiment” or “recommendations.” Various features or grams maybelong to one or more drivers. For example, the feature “I love it” maybe a member of the “positive sentiment” driver, whereas the feature “Irecommend it” may be a member of the “recommendations” driver.

The drivers (also sometimes referred to as attributes or moves) may besub-divisions of the categories that better identify the motivation ofgroups of themes. For example, the following grams all indicate futureintent to purchase: “will keep purchasing,” “continue to buy,” “I wouldbuy,” “I will buy,” “would definitely buy.” Categories (e.g., Excellent,Bad, Questionable) may sub-divided into driver frames according toidentified drivers (e.g., future purchase intent, direct recommendation,cognitive-surprise, etc.). Process 1200 is a process for identifyingdrivers and assigning grams/features to a driver group.

Still referring to FIG. 12, process 1200 is shown to include estimatingone or more driver groups present in a set of documents (step 1202).Step 1202 may include estimating general driver groups that are likelyto be present in the document set. For example, if the documents areconsumer reviews of retail products, step 1202 may include estimatingdriver groups for recommendations, emotive responses, past experiences,etc.

Process 1200 is shown to include assigning grams to a driver group (step1204). If a gram does not correspond to any driver group, a new drivergroup may be added (step 1206). In some embodiments, if the number ofgrams in a driver group exceeds a threshold value, the driver group issplit into multiple driver groups (step 1208). In some embodiments,multiple driver groups may be combined into a single driver group inresponse to a determination that the number of grams in the drivergroups are less than a threshold value (step 1210).

Process 1200 may be performed multiple times as the latent drivertaxonomy is derived from the data. In some embodiments, process 1200 mayproduce a three-level taxonomy having a temporal level, an entity level,and a terminal level.

At the highest level, drivers may have a temporal aspect. The temporalaspect may indicate whether the driver is past, present, or future. Forexample, consumer reviews may include (1) a discussion of product orpersonal experiences prior to the testing (e.g., “I used to hateshowering but . . . ”), (2) a discussion of product or personalexperiences that occur contemporaneously with the testing (e.g., “Iliked it very much,” “it is easy to use,” “I was pleasantly surprised”),or (3) a discussion or insight as to future product or personalexperiences (e.g., “I recommend it,” “I will be buying it again,” “thisis a winner”).

At the intermediate level, drivers may have an entity aspect. The entityaspect may indicate whether the driver is personal or product-based.Consumer reviews may be focused more relatively to the product or morerelatively to the personal experience. For example, the consumer review“I used to hate showering but . . . ” is related to a personalexperience. However, the consumer review “I liked it very much” isobviously from the person but is indicative that that the product isgood/useful/positive. The consumer review “I was pleasantly surprisedinforms” indicates a cognitive reaction; “I recommend it” is a personalappraisal; “I will be buying it again” is a personal intent; and “easyto use” describes a functional aspect of the product.

At the terminal level is the actual driver. The actual driver may be acategory of sentiment expressed in the text. For example, “pleasantlysurprised” is a “current>personal>cognitive-surprise” driver, whereas“will be buying it again” is a “future>personal>purchase-intent” driver,and “easy to use” is a “current>product>efficacy” driver.

Referring now to FIG. 13, a flowchart of a process 1300 for scoringdocuments using rule-based criteria is shown, according to an exemplaryembodiment. Process 1300 may be performed by one or more components ofthe systems described with reference to FIGS. 1-8R. Process 1300 may beperformed to assign a numerical score to a document based on the text ofthe document. In brief overview, a document is parsed to identify gramsin the document. The grams are extracted from the document. Thecategories of the grams (e.g., Excellent, Good, Fair, Questionable, Bad,Terrible, etc.) in the document are identified and the quantity of gramsin each category is compiled. A rule-based procedure assigns a constructscore to the document based on the number of grams (extracted from thedocument) in each category.

Still referring to FIG. 13, process 1300 is shown to include analyzingtextual data in a document to identify one or more grams present in thetextual data (step 1302). The grams may be generated, expanded, and/orvalidated using process 1000. Grams may be tested for accuracy usingprocess 1100 and assigned to driver groups using process 1200. The gramsused in step 1302 may be the result of processes 1000-1200. Step 1302may include parsing the textual data of a document for occurrences ofgrams (i.e., text strings) in the document.

Still referring to FIG. 13, process 1300 is shown to include identifyinga category associated with each identified gram (step 1304). Categoriesmay include Excellent, Good, Fair, Questionable, Bad, and Terrible.Grams in the Excellent and Good categories indicate a positive viewtowards what is being discussed. The difference between Excellent andGood is the degree of confidence that the feature indicates the text'spositivity. For example, the gram “I totally recommend this product” maybe a member of the Excellent category, whereas the gram “workseffectively” may be a member of the Good category. The categories ofTerrible and Bad may be the opposite of Excellent and Good in that theirpresence indicates a “negative view” in the text. For example, the gram“definitely won't purchase” may be a member of the Terrible category,whereas the gram “would not advise” may be a member of the Bad category.

Grams in the Questionable category may be positive or negative,depending on context. The presence of questionable grams in a texttherefore adds a degree of ambiguity in classification. For example, thegram “I am not quite sure” may be a Questionable gram. Grams in the Faircategory, although they are not actually negative, have the potential toindicate either a positive or negative view, depending on the context.An example of a Fair gram is “it's ok.” If the text includes multipleBad or Terrible grams along with “it's ok,” then this Fair gram mayindicate a negative view. However, if the same gram “it's ok” is usedalong with multiple Good or Excellent grams, then the Fair gram mayindicate a positive view. Fair grams may supplement the group of gramsthat best categorizes the text prior to their inclusion.

Grams may be pre-assigned to categories prior to performing process 1300(e.g., in step 1008 of process 1000). In some embodiments, step 1304includes identifying the category to which each of the identified gramswas previously assigned. The category to which a gram is assigned may beassociated with the gram and identified in step 1304.

Still referring to FIG. 13, process 1300 is shown to include calculatinga quantity of grams in the textual data associated with each identifiedcategory (step 1306). Step 1306 may include determining the total numberof grams in each general category (i.e., Excellent, Good, Fair,Questionable, Bad, and Terrible) that are present in the textual data.For example, if the textual data includes four grams which are membersof the Good category (G) and one gram that is a member of the Excellentcategory (E), step 1306 may include summing the total number of grams ineach identified category to calculate E=1 and G=2.

In some embodiments, step 1306 includes combining one or more categoriesof grams and calculating a total number of grams in the textual datathat are members of the combined category. For example, step 1306 mayinclude defining a new category “Less than Good” (LG) which includes allof the categories Questionable, Bad, and Terrible (i.e., all thecategories which are less than the Good category in terms of providingevidence of the corresponding construct). In other words, LG grams mayinclude all of the Questionable grams, all of the Bad grams, and all ofthe Terrible grams that are identified in the document. The number of LGgrams in a text may be relevant because the presence of LG grams canoffset the presence of highly positive components of the text.

Categories may be referred to by the first letter of their names. Thus,Excellent is E, Good is G, Fair is F, Bad is B, Terrible is T, Less thanGood is LG, etc. As used herein, the variable CX refers to an arbitraryconstruct “Construct X.” Construct X may be any construct for which thescore is currently being calculated in process 1300. Some exemplaryconstructs include happiness, confusion, frustration, bitterness,cheerfulness, thoughtfulness, impactfulness, excitement, enthusiasm,gratitude, trust, contempt, remorse, irritation, disappointment, anger,outrage, embarrassment, behavior shift, dynamic themes, boo, not happy,not grateful, surprise, shock, crave, desire, unmet needs, met needs,value, and/or other sentiments or mental states that can be expressed ina document.

Still referring to FIG. 13, process 1300 is shown to include applyingscoring rules to the calculated quantities of grams to determine aconstruct score for the document (step 1308). In some embodiments,process 1300 uses a scoring scale from 0-10 where a score of 10indicates clear positive evidence of CX and a score of 0 indicates clearnegative evidence of CX. In some embodiments, the document is checked toassess whether it is characteristic of a prototypical CX text or anon-prototypical CX text. If the document is not characteristic ofeither category then the document is may be given a preliminary score ofneutral (e.g., 5 on a scale from 0-10) to indicate initial neutrality orunknown.

High-scoring CX texts (e.g., 10's, 9's, and 8's) have both clearpositive evidence of CX and no non-positive evidence. The followingequation demonstrates an exemplary rule-based scoring method for suchdocuments:

$W = \left\{ \begin{matrix}10 & {{{{if}\mspace{14mu} E} > 0},} & {{LG} = 0} \\9 & {{{{if}\mspace{14mu} G} > 2},} & {{LG} = 0} \\8 & {{{{if}\mspace{14mu} G} > 1},} & {{LG} = 0}\end{matrix} \right.$

The preceding equation describes the process for allocating initialscores of W=10, W=9, and W=8, where W is the score assigned to CX. Thus,for example, if the identified number of Excellent features (E) isgreater than 0 and the identified number of features that are less thangood (LG) is equal to zero, then an initial CX score of 10 is given. Therationale for this scoring is that a text would be given an initialmaximum CX score of 10 if it contains a lot of positive evidence and nocounter-evidence. As shown in the scoring equation, a maximum score of10 cannot be attained without at least one piece of clear Excellentevidence of CX (e.g., E>0) and any contrary evidence (e.g., LG>0 or E=0)renders a full score non-possible. In this way, high confidence can bemaintained that scores of 10 are assigned to only the most clearlypositive documents.

The above equation applies only to documents that have only positiveevidence (e.g., LG=0). However, many documents will have at least somenegative or non-positive evidence. The rules for scoring documents withat least some negative evidence may be more detailed. CX scores of 5through 8 may be calculated in the event that both positive andnon-positive evidence are identified. The process for calculating scoresof 5-8 may involve calculating a temporary CX score (CX₁) in step 1308and then calculating a second CX score in step 1310. The temporary CX₁score calculated in step 1308 may consider the importance of thenon-positive evidence, whereas the second CX score calculated in step1310 may consider the importance of the positive evidence.

For CX scores of 5 through 8, step 1308 considers two primary cases:Case A: E>LG>0Case B: G>LG>0 AND LG≧E

For Case A, the number of Excellent features (E) is greater than thenumber of identified less than good features (LG), which in turn isgreater than zero. Therefore, in Case A, if LG is equal to 1, then Emust be greater than 1. For Case B, two criteria must be met. First, thenumber of identified Good features (G) is greater than the number ofidentified less than good features (LG), which in turn is greater thanzero. Therefore, in Case B, if LG is equal to 1, then G must be greaterthan 1. Additionally, the second criterion requires that LG≧E. Thisextra requirement means that the number of identified elements of G isgreater than the number of identified elements of E. In other words, thepurpose of Case A is to determine if the positive elements of a documentare more Excellent features than Good features, and the purpose of CaseB is to determine if the positive elements of a document are more Goodfeatures than Excellent features. Both rules may ensure that a documentis not double-scored.

Given the above two cases, step 1308 may be performed to generate atemporary score of CX₁ based on non-positive evidence. The CX₁ score maybe modified based on the type of positive evidence (either Excellent orGood) in step 1310.

As noted above, the category LG consists of the categories ofQuestionable, Bad, and Terrible. The negative impact of the number of LGgrams on the final CX score may depend largely on what the individualcategory of LG grams (e.g., Questionable, Bad, and Terrible), and howmuch evidence is identified for each LG category. The following equationshows how initial CX₁ scores of 5 to 8 can be evaluated:

${Cx}_{1} = \left\{ \begin{matrix}8 & {{{{if}\mspace{14mu} T} = 0},{B = 0},{Q = 1}} \\7 & {{{{if}\mspace{14mu} T} = 0},{B = 0},{Q > 1}} \\6 & {{{{if}\mspace{14mu} T} = 0},{{B > {0\mspace{14mu}{or}\mspace{14mu} T}} = 1}} \\5 & {{{if}\mspace{14mu} T} > 1}\end{matrix} \right.$

For example, according to the equation for calculating initial CX₁values of 5-8, CX₁ will equal the value of 8 if the number of identifiedTerrible features is equal to 0, the number of identified Bad featuresis equal to 0, and the number of identified Questionable features isequal to 1. Note also in this example that since LG>0 (according to thecriteria for Case A or Case B), then at least one of the T, B, and Qgrams must be present at least once. Additionally, since either E>LG(Case A) or G>LG (Case B) and the Terrible grams are members of LG, ifT>1, then E or G (individually or collectively) must be greater than Tfor the equation to be satisfied.

If none of the aforementioned scoring criteria are met, then step 1308considers the opposite scenario in order to generate scores from 0through 5. The rule-based scoring criteria for generating scores from 0through 5 may be the negative counterparts of the scoring criteria forgenerating scores from 5 through 10. For example, all of the aboveequations still apply with E's replaced by T's, G's replaced by B's, andLG's replaced by AB's (where AB means “above bad”). The scores assignedby the negative counterpart equations may be adjusted by a rule of 10−s,where s is the score in the positive counterpart equation. For example,10 is replaced by 0 (i.e., 10-10), 9 is replaced by 1 (i.e., 10-9), 8 isreplaced by 2 (i.e., 10-8), 7 is replaced by 3 (i.e., 10-7), 6 isreplaced by 4 (i.e., 10-6) and 5 remains at 5 (i.e., 10-5). The negativecounterpart equations are provided as follows:

$W = \left\{ {{\begin{matrix}0 & {{{{if}\mspace{14mu} T} > 0},} & {{AB} = 0} \\1 & {{{{if}\mspace{14mu} B} > 2},} & {{AB} = 0} \\2 & {{{{if}\mspace{14mu} B} > 1},} & {{AB} = 0}\end{matrix}{Cx}_{2}} = \left\{ \begin{matrix}2 & {{{{if}\mspace{14mu} E} = 0},{G = 0},{Q = 1}} \\3 & {{{{if}\mspace{14mu} E} = 0},{G = 0},{Q > 1}} \\4 & {{{{if}\mspace{14mu} E} = 0},{{G > {0\mspace{14mu}{or}\mspace{14mu} E}} = 1}} \\5 & {{{if}\mspace{14mu} E} > 1}\end{matrix} \right.} \right.$

The negative counterpart equations are used to assign scores between 0and 5, where 0 is the lowest possible score and CX₂ is the temporaryscore assigned to negative documents. For scores between 0 and 5, thetemporary CX₂ score calculated in step 1308 may consider the importanceof the non-negative evidence, whereas the second CX score calculated instep 1310 may consider the importance of the negative evidence.

Still referring to FIG. 13, process 1300 is shown to include applyingadditional scoring rules to adjust the construct score (step 1310). Asnoted above, step 1308 may be performed to calculate a temporary CX₁score (e.g., CX₁), which considers the importance of the non-positiveevidence. Step 1310 may be performed to adjust the temporary score CX₁based on non-positive evidence.

For temporary CX₁ scores of 5-8, step 1310 may adjust the temporary CX₁score based on whether Case A or Case B has been met. For example, thefollowing equation describes the effect of Case A (i.e., E>LG>0):

${{{if}\mspace{14mu} E} > {LG} > 0},{{{then}\mspace{14mu}{CX}} = \left\{ \begin{matrix}{CX}_{1} & {{{if}\mspace{14mu} G} = 0} \\{{CX}_{1} + \alpha_{1}} & {{{if}\mspace{14mu} G} = 1} \\{{CX}_{1} + \alpha_{2}} & {{{if}\mspace{14mu} G} > 1}\end{matrix} \right.}$

In some embodiments, the parameters α₁ and α₂ have the values α₁=1 andα₂=1.5. In other embodiments, α₁ and α₂ have different (e.g., greater orlesser) values. In some embodiments, α₁ and α₂ can be automatically ormanually adjusted (e.g., tuned, updated, dynamically or adaptivelyadjusted, etc.) to tune the scoring algorithm. For example, step 1104 ofprocess 1100 may include adjusting parameters α₁ and α₂ in response to adetermination that the number of texts assigned an inaccurate scoreexceeds a threshold value.

For Case A to apply, the number of Excellent grams is greater than thenumber of LG grams. As shown in the above equation, if the value of G isgreater than 1, then the temporary CX₁ score may be increased by α₂. Ifthe value of G is equal to 1, then the temporary CX₁ score may beincreased by α₁. If the value of G is equal to 0, then the temporary CX₁score may not be increased or decreased.

The following equation describes the effect of Case B (i.e., G>LG>0 andLG≧E):

${{{if}\mspace{14mu} G} > {LG} > 0},{{{then}\mspace{14mu}{LG}} \geq E},{{{then}\mspace{14mu}{CX}} = \left\{ \begin{matrix}{{CX}_{1} + \alpha_{3}} & {{{if}\mspace{14mu} E} = 0} \\{CX}_{1} & {{{if}\mspace{14mu} E} > 0}\end{matrix} \right.}$

In some embodiments, the parameter α₃ has the value α₃=−1. In someembodiments, α₃ can be automatically or manually adjusted (e.g., tuned,updated, dynamically or adaptively adjusted, etc.) to tune the scoringalgorithm. For example, step 1104 of process 1100 may include adjustingparameters α₃ in response to a determination that the number of textsassigned an inaccurate score exceeds a threshold value.

For Case B to apply, the number of Good grams is greater than the numberof Excellent grams. Also, G must be greater than LG. Given that Gevidence is primarily providing the positive score, the value of E mayaffect whether the temporary CX₁ score is adjusted or not adjusted. Forexample, if E is equal to zero then the positive evidence is not strongand the value of CX is calculated by adding α₃ to the temporary CX₁score. Since α₃ is a negative number (e.g., α₃=−1), the lack of strongpositive evidence (e.g., E=0) functions to decrease the CX score.

For CX₂ scores from 0 to 5, step 1310 may use the negative counterpartsof the adjustment equations provided above. The negative counterpartequations are provided as follows:

${CX} = \left\{ {{\begin{matrix}{CX}_{2} & {{{if}\mspace{14mu} B} = 0} \\{{CX}_{2} - \alpha_{1}} & {{{if}\mspace{14mu} B} = 1} \\{{CX}_{2} - \alpha_{2}} & {{{if}\mspace{14mu} B} > 1}\end{matrix}{CX}} = \left\{ \begin{matrix}{{CX}_{2} - \alpha_{3}} & {{{if}\mspace{14mu} T} = 0} \\{CX}_{2} & {{{if}\mspace{14mu} T} > 0}\end{matrix} \right.} \right.$

In some embodiments, step 1310 includes applying rightedness criteria tothe CX scores. Rightedness considers the location at which the finalinstance of an identified gram occurs. For example, the final instanceof an Excellent gram is to the right of the final instance of any LGgram, then step 1310 may include increasing the CX score by 1 point. Ifthe final instance of a Terrible gram is to the right of the finalinstance of any AB gram, then step 1310 may include decreasing the CXscore by 1 point.

Referring now to FIG. 14, a flowchart of a process 1400 for assigning a“thoughtfulness” score to a document is shown, according to an exemplaryembodiment. Processes 1400 may be performed by one or more components ofthe systems described with reference to FIGS. 1-8R. Thoughtfulness is aconstruct defined by the characteristics of being detailed, considered,reflective, organized, crafted, reasoned, contemplative, ordemonstrative of deeper levels of cognitive processing. As such, textshaving a high thoughtfulness score are counter-characterized by thedegree to which they are spontaneous, extemporaneous, visceral,impactful, immediate, intensive, and/or emotive. A text with a highthoughtfulness metric score is not merely good, it is better than good.

Texts with a high thoughtfulness score often include considered (hencecognitive) responses more frequently than overt positive claims. Forexample, high-scoring thoughtfulness metric texts may include somedetail or mention of how the product benefited, changed, or impacted aparticipant's personal experience. Additionally, texts with a highthoughtfulness score may include a recommendation (direct or indirect)of the product and/or an intent for personal future use/purchase of theproduct. Texts with a high thoughtfulness score may include a priorexperience with a similar product. In either case, this temporalperspective demonstrates that the text is more “thought out” andconsequently that the writer's mental model of the product type may havebeen influenced.

Still referring to FIG. 14, process 1400 is shown to include identifyinga construct score for a document (step 1402). Process 1400 may acceptthe construct score CX determined in process 1300 as an input, or mayaccept a general indication of whether the document is generallypositive or generally negative as an alternative to the construct scoreCX. The construct score may be a numerical score from 0 through 10 asdescribed with reference to FIG. 13.

Still referring to FIG. 14, process 1400 is shown to include calculatinga “thoughtfulness starting score” WM₀ based on the construct score (step1404). Step 1404 may include assigning a maximum score (e.g., 10) todocuments with a positive construct score (e.g., a score from 6-10) andassigning a minimum score (e.g., 0) to documents with a negativeconstruct score (e.g., a score from 0-4). Documents with a neutralconstruct score (e.g., 5) may be assigned the same thoughtfulnessstarting score (e.g., 5). An equation describing the calculationperformed in step 1404 is provided as follows:

${WM}_{0} = \left\{ \begin{matrix}10 & {{{if}\mspace{14mu} 10} \geq {CX} > 5} \\0 & {{{if}\mspace{14mu} 0} \leq {CX} < 5} \\5 & {otherwise}\end{matrix} \right.$where WM₀ is the thoughtfulness metric starting score and CX is theconstruct score.

Still referring to FIG. 14, process 1400 is shown to include determininga quantile score for the document (step 1406). The quantile score mayrepresent a proportion of the document text that consists of unknownthemes (i.e., text strings that do not match any identified gram orfeature). Prior to step 1406, a variety of drivers and their associatedthemes that are present in the document may be detected. This detectionallows the system to know how much of the text is identified and,therefore, how much of the text is made up of unknown themes. The partsof the text that are unknown may be relatively unique language thatevidentially supports those elements of the texts that the system hasbeen able to identify. Thus, if a text is generally positive based onthe known themes (e.g., assigned a CX score of 6-10), the unknown themesmay support the known themes in expressing a generally positivesentiment.

In some embodiments, step 1406 includes calculating a proportion of thedocument text that consists of unknown themes. The proportion of unknownthemes may be calculated by first forming a baseline estimate for theproportion of the text that is estimated to contain known themes (e.g.,a ratio of the number of words that are part of known themes to thetotal length of the document). Once the known-themes proportion of thetext is identified, the number of unknown themes can be estimated fromthe length and the proportion of known themes (e.g., the length of theremaining text multiplied by the ratio of known themes).

In some embodiments, step 1406 includes assigning documents with thefewest number of unknown themes a low quantile score. For example, thedocuments with the shortest 5% of unknown themes may be assigned a scoreof 0. Quantile scores may be assigned to the remaining documents basedon the length and/or number of unknown themes. For example, documentswith the next shortest 15% of unknown themes may be assigned a quantilescore of 1, documents with the middle 50% of unknown themes (based onknown theme length and/or number) may be assigned a quantile score of 2,documents with the next 15% of unknown themes may be assigned a quantilescore of 3, and documents with the longest 5% of unknown themes may beassigned a quantile score of 4. In various embodiments, the quantilescores and/or scoring criteria may be adjusted or more finely tuned. Forexample, dividing the texts into more quantiles may allow quantilescores to be defined with more precision.

Still referring to FIG. 14, process 1400 is shown to include using thethoughtfulness starting score WM₀ and the quantile score q_(n) tocalculate a thoughtfulness score for the document (step 1408). In someembodiments, step 1408 includes using the following equation tocalculate the thoughtfulness score M_(t):

$M_{t} = \left\{ \begin{matrix}{6 + q_{n}} & {{{if}\mspace{14mu}{WM}_{0}} = 10} \\{4 - q_{n}} & {{{if}\mspace{14mu}{WM}_{0}} = 0} \\5 & {otherwise}\end{matrix} \right.$where q_(n) is the quantile score for the text and WM₀ is thethoughtfulness starting score calculated in step 1404. According to theabove equation, if the text has a baseline score of 10, itsthoughtfulness score M_(t) is 6+q_(n). If the text has a baseline scoreof 0, then its thoughtfulness score M_(t) is 4−q_(n).

Note that in these calculations, the starting thoughtfulness score iseither 10 or 0. Texts which scored 5 are not affected at this stage. Thecalculations include subtracting (for positives) or adding (fornegatives) the quantity 4−q_(n) to the starting thoughtfulness score.The significance of this quantity is that it represents the differencebetween the quantile score of the longest texts (q_(n)=4) and thequantile score q_(n) for the text at issue. Step 1408 effectivelypenalizes the text's thoughtfulness score for brevity.

Still referring to FIG. 14, process 1400 is shown to include applyingscoring rules to adjust the thoughtfulness score (step 1410). Prior tostep 1410, the number and type of Excellent and Terrible features havenot been considered. The rightedness of the identified features has alsonot been considered. To account for these features, process 1400 usesthe following equation to adjust the thoughtfulness score calculated instep 1408:

if  6 ≤ M_(t) ≤ 10, then $M_{t}^{\prime} = \left\{ \begin{matrix}{M_{t} - 1} & {{{{{if}\mspace{14mu} E} < 2},{{or}\mspace{14mu}{text}\mspace{14mu}{is}\mspace{14mu}{not}\mspace{14mu}{positive}\mspace{14mu}{righted}}}\mspace{20mu}} \\M_{t} & {otherwise}\end{matrix} \right.$

The preceding equation applies to M_(t) scores of 6-10, where M_(t) isthe thoughtfulness score calculated in step 1408. According to thisequation, a text's corrected thoughtfulness score M_(t)′ can be adjusteddown by a point or any other decrement (e.g., half a point, one and ahalf points, two points, etc.). This adjustment occurs if the text doesnot contain at least 2 pieces of positive evidence from the Excellentcategory and the text's rightedness is not positive (e.g., the last gramis not positive). The lower bound score may be fixed at 6 so that a textremains as positive with the lowest possible score positive.

Negative scores (0 to 4) are calculated in the same way as positivescores with two exceptions. First, the Excellent category is replaced bythe Terrible category. Second, all adjustments require just one piece ofevidence for Terrible because of the relative lack of unknown themesthat is a characteristic of negative texts. The following equation isused to calculate adjusted thoughtfulness scores for M_(t) scores from0-4:

if  0 ≤ M_(t) ≤ 4, then $M_{t}^{\prime} = \left\{ \begin{matrix}{M_{t} - 1} & {{{{{if}\mspace{14mu} T} < 1},{{or}\mspace{14mu}{text}\mspace{14mu}{is}\mspace{14mu}{not}\mspace{14mu}{positive}\mspace{14mu}{righted}}}\mspace{20mu}} \\M_{t} & {otherwise}\end{matrix} \right.$

Referring now to FIG. 15, a flowchart of a process 1500 for calculatingan “impactfulness” score for a document is shown, according to anexemplary embodiment. Process 1500 may be performed by one or morecomponents of the systems described with reference to FIGS. 1-8R.Impactfulness is a construct defined by the characteristics of beingspontaneous, extemporaneous, visceral, impactful, immediate, intensive,and/or emotive (especially in terms of happiness and positive surprise).As such, texts with a high impactfulness score are counter-characterizedby the degree to which they are detailed, considered, reflective,organized, crafted, reasoned, contemplative, or demonstrative of deeperlevels of cognitive processing.

Texts with a high impactfulness score can be described as beingemotionally driven, even while emotion words themselves may sometimes beabsent. The more a text appears to be thought out or demonstratessupporting evidence for the claims made within the text, the less thetext is deemed to be characteristic of a high impactfulness text (eventhough it will remain positive in terms of Construct X andthoughtfulness). Texts with a high impactfulness score are not merelygood, they are better than good: The product/service is better than mostcomparable alternatives on the market (especially the user'scurrent/previous product/service). The impactfulness score for a textmay be correlated with purchase intent.

Texts with a high impactfulness score can be quantitatively identifiedby reversing the logic of unknown themes that was applied in process1400. For example, if a text is overwhelmingly composed of known themes,then its personalized unique contribution is negligible. Such documentsmay have been written relatively quickly and may be weakly supported bylife-experience evidence. Texts with a high impactfulness score texts,although overwhelmingly positive, may not provide clear evidence thatsuch an experience has transitioned into a significant life change ormental model change.

Still referring to FIG. 15, process 1500 is shown to include identifyinga construct score for a document (step 1502). Process 1500 may acceptthe construct score CX determined in process 1300 as an input, or mayaccept a general indication of whether the document is generallypositive or generally negative as an alternative to the construct scoreCX. The construct score may be a numerical score from 0 through 10 asdescribed with reference to FIG. 13.

Still referring to FIG. 15, process 1500 is shown to include calculatingan impactfulness starting score based on the construct score (step1504). Step 1504 may include assigning a maximum score (e.g., 10) todocuments with a positive construct score (e.g., a score from 6-10) andassigning a minimum score (e.g., 0) to documents with a negativeconstruct score (e.g., a score from 0-4). Documents with a neutralconstruct score (e.g., 5) may be assigned the same impactfulnessstarting score (e.g., 5). An equation describing the calculationperformed in step 1504 is provided as follows:

${WM}_{0} = \left\{ \begin{matrix}10 & {{{if}\mspace{14mu} 10} \geq {CX} > 5} \\0 & {{{if}\mspace{14mu} 0} \leq {CX} < 5} \\5 & {otherwise}\end{matrix} \right.$where WM₀ is the impactfulness starting score and CX is the constructscore.

Still referring to FIG. 15, process 1500 is shown to include determininga quantile score for the document (step 1506). The quantile score mayrepresent a proportion of the document text that consists of unknownthemes (i.e., text strings that do not match any identified gram orfeature). Prior to step 1506, a variety of drivers and their associatedthemes that are present in the document may be detected. This detectionallows the system to know how much of the text is identified and,therefore, how much of the text is made up of unknown themes. The partsof the text that are unknown may be relatively unique language thatevidentially supports those elements of the texts that the system hasbeen able to identify. Thus, if a text is generally positive based onthe known themes (e.g., assigned a CX score of 6-10), the unknown themesmay support the known themes in expressing a generally positivesentiment.

In some embodiments, step 1506 includes calculating a proportion of thedocument text that consists of unknown themes. The proportion of unknownthemes may be calculated by first forming a baseline estimate for theproportion of the text that is estimated to contain known themes (e.g.,a ratio of the number of words that are part of known themes to thetotal length of the document). Once the known-themes proportion of thetext is identified, the number of unknown themes can be estimated fromthe length and the proportion of known themes (e.g., the length of theremaining text multiplied by the ratio of known themes).

In some embodiments, step 1506 includes assigning documents with thefewest number of unknown themes a low quantile score. For example, thedocuments with the shortest 5% of unknown themes may be assigned a scoreof 0. Quantile scores may be assigned to the remaining documents basedon the length and/or number of unknown themes. For example, documentswith the next shortest 15% of unknown themes may be assigned a quantilescore of 1, documents with the middle 50% of unknown themes (based onknown theme length and/or number) may be assigned a quantile score of 2,documents with the next 15% of unknown themes may be assigned a quantilescore of 3, and documents with the longest 5% of unknown themes may beassigned a quantile score of 4. In various embodiments, the quantilescores and/or scoring criteria may be adjusted or more finely tuned. Forexample, dividing the texts into more quantiles may allow quantilescores to be defined with more precision.

Still referring to FIG. 15, process 1500 is shown to include using theimpactfulness starting score and the quantile score to calculate animpactfulness score M_(i) for the document (step 1508). In someembodiments, step 1508 includes using the following equation tocalculate the impactfulness score:

$M_{t} = \left\{ \begin{matrix}{10 - q_{n}} & {{{if}\mspace{14mu}{WM}_{0}} = 10} \\q_{n} & {{{if}\mspace{14mu}{WM}_{0}} = 0} \\5 & {otherwise}\end{matrix} \right.$where q_(n) is the quantile score for the text and WM₀ is theimpactfulness starting score calculated in step 1504. According to theabove equation, if the text has a baseline score of 10, itsimpactfulness score is 10−q_(n). If the text has a baseline score of 0,then its impactfulness score is q_(n).

Note that in these calculations, we start with an impactfulness startingscore of either 10 or 0 (texts which scored 5 are not a affected at thisstage). Then we subtract (for positives) or add (for negatives) thequantity q_(n) to the starting impactfulness score. Step 1508effectively penalizes the text's impactfulness score for its presence ofpotentially cognitively reasoned evidence.

Still referring to FIG. 15, process 1500 is shown to include applyingscoring rules to adjust the impactfulness score (step 1510). Prior tostep 1510, the number and type of Excellent and Terrible features havenot been considered. The rightedness of the identified features has alsonot been considered. To account for these features, process 1500 usesthe following equation to adjust the impactfulness score calculated instep 1508:

if  6 ≤ M_(i) ≤ 10, then $M_{i}^{\prime} = \left\{ \begin{matrix}{M_{i} - 1} & {{{{{if}\mspace{14mu} E} < 1},{{or}\mspace{14mu}{text}\mspace{14mu}{is}\mspace{14mu}{not}\mspace{14mu}{positive}\mspace{14mu}{righted}}}\mspace{20mu}} \\M_{i} & {otherwise}\end{matrix} \right.$

The preceding equation applies to M_(i) scores of 6-10, where M_(i) isthe impactfulness score calculated in step 1508. According to thisequation a text's corrected impactfulness score M_(i)′ can be adjusteddown by a point or any other decrement (e.g., half a point, one and ahalf points, two points, etc.). This adjustment occurs if the text doesnot contain at least 1 piece of positive evidence from the Excellentcategory and the text's rightedness is not positive (e.g., the last gramis not positive). The lower bound score may be fixed at 6 so that a textremains as positive with the lowest possible score. The impactfulnessscore requires only one piece of Excellent evidence (as opposed to 2 forthe thoughtfulness score) to avoid being decremented due to the relativebrevity of typical texts with a high impactfulness score.

Negative scores (0 to 4) are calculated in the same way as positivescores with two exceptions. First, the Excellent category is replaced bythe Terrible category. Second, all adjustments require just one piece ofevidence for Terrible because of the relative lack of unknown themesthat is a characteristic of negative texts. The following equation isused to calculate adjusted impactfulness scores for M_(i) scores from0-4:

if  0 ≤ M_(i) ≤ 4, then $M_{i}^{\prime} = \left\{ \begin{matrix}{M_{i} - 1} & {{{{{if}\mspace{14mu} T} < 1},{{or}\mspace{14mu}{text}\mspace{14mu}{is}\mspace{14mu}{not}\mspace{14mu}{positive}\mspace{14mu}{righted}}}\mspace{20mu}} \\M_{i} & {otherwise}\end{matrix} \right.$

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements may bereversed or otherwise varied and the nature or number of discreteelements or positions may be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepsmay be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions may be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems, and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure may be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data, which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures may show a specific order of method steps, theorder of the steps may differ from what is depicted. Also two or moresteps may be performed concurrently or with partial concurrence. Suchvariation will depend on the software and hardware systems chosen and ondesigner choice. All such variations are within the scope of thedisclosure. Likewise, software implementations could be accomplishedwith standard programming techniques with rule based logic and otherlogic to accomplish the various connection steps, processing steps,comparison steps and decision steps.

What is claimed is:
 1. A system for conducting parallelization of tasks,comprising: an interface for receiving messages, each message comprisinga representation of logic describing a plurality of tasks to be executedin parallel, and a content payload to be processed by conducting theplurality of tasks; and a parallel processing grid comprising devicesrunning on independent machines, each device comprising a processingmanager unit and a plurality of processing units, wherein the processingmanager unit is configured to parse the received messages and todistribute each of the plurality of tasks to one of the plurality ofprocessing units for independent and parallel processing relative to thecontent payload, each of the plurality of tasks corresponding to adifferent processing unit, each processing unit completing thecorresponding task asynchronously with another processing unit, whereinidentification of parallel tasks comprises a listing of the tasks to becompleted in parallel and wherein each item of the list refers to afunction to be executed by the processing units; wherein the processingunits are general purpose processing units configured to identify thefunction and to recall resources for executing the function from aplug-in library, wherein the function is an emotional scoring function,wherein the emotional scoring function is a task in parallel with acontext determination task.
 2. The system of claim 1, furthercomprising: a messaging source having a website crawler, the websitecrawler configured to generate the message having the representation oflogic describing parallel tasks and the content payload for use in theparallel tasks.
 3. The system of claim 1, further comprising: amessaging source having a streaming data interface for receivingstreaming data, wherein the messaging source is configured to processthe streaming data and to generate the message using the streaming data.4. The system of claim 3, wherein the messaging source is configured touse the streaming data to create the payload and wherein theidentification of the tasks is not a part of the streaming data.
 5. Thesystem of claim 1, further comprising: a messaging source comprising aquery engine for querying a data source and for generating a series ofthe messages using the query results, wherein identification of thetasks is not a part of the query result data.
 6. The system of claim 1,wherein the interface comprises a framework manager configured to queuethe messages, and wherein each processing manager unit is configured torequest new messages from the queue when resources permit.
 7. The systemof claim 1, wherein the messages are not compiled and do not include thesource code for the tasks, and wherein the messages utilize a mark-uplanguage to identify the parallel tasks and the content payload.
 8. Thesystem of claim 7, wherein the content payload comprises a reference todata not embedded within the message.
 9. The system of claim 8, whereinthe function comprises a first emotional scoring function for a firstemotion and another task includes a second emotional scoring functionfor a second emotion, wherein both of the first and second emotionalscoring functions are executed as parallel tasks and represented asparallel tasks in the logic of the message.
 10. The system of claim 1,wherein the function comprises a first emotional scoring function for afirst emotion and another task includes a second emotional scoringfunction for a second emotion, wherein both of the first and secondemotional scoring functions are executed as parallel tasks andrepresented as parallel tasks in the logic of the message.
 11. Acomputerized method for processing tasks, comprising: receiving amessage describing at least two processing tasks to be parallelized andcompleted relative to a payload content of the message; parsing thereceived message to identify the at least two tasks for parallelization;distributing the plurality of tasks, in parallel, to a pluralitydiscrete processing units, each of the plurality of tasks correspondingto a different discrete processing unit; at each discrete processingunit, completing the entirety of its corresponding task asynchronouslywith another discrete processing unit, wherein identification of theparallel tasks comprises a listing of the tasks to be completed inparallel and wherein each item of the list refers to a function to beexecuted by the processing units; wherein the discrete processing unitsare general purpose processing units configured to identify the functionand to recall resources for executing the function from a plug-inlibrary, wherein the function is an emotional scoring function, whereinthe emotional scoring function is a task in parallel with a contextdetermination task.
 12. The method of claim 11, further comprising:generating the message having the representation of logic and thecontent payload by crawling a changing web-based data source andcreating a message for discrete updates to the data source.
 13. Themethod of claim 11, further comprising: generating the message usingstreaming data to create the payload and wherein identification of thetasks is not a part of the streaming data.
 14. The method of claim 11,further comprising: generating the message using a query engine, byquerying a data source and generating a series of the messages using thequery results, wherein the identification of the tasks are not a part ofthe query result data.
 15. The method of claim 11, wherein a processingmanager unit conducts the receiving, parsing, and distribution steps,the method further comprising: queuing the received messages at aframework manager; at each processing manager unit, requesting newmessages from the queue when resources permit.
 16. The method of claim11, wherein the messages is not compiled and do not include the sourcecode for the tasks, and wherein the messages utilize a mark-up languageto identify the tasks to be completed in parallel and the contentpayload.
 17. The method of claim 16, wherein the content payloadcomprises a reference to data not embedded within the message.
 18. Themethod of claim 17, wherein the function comprises a first emotionalscoring function for a first emotion and another task includes a secondemotional scoring function for a second emotion, wherein both of thefirst and second emotional scoring functions are executed as paralleltasks and represented as parallel tasks in the logic of the message. 19.The method of claim 11, wherein the function comprises a emotionalscoring function for a first emotion and another task includes anemotional scoring function for a second emotion, wherein both of thefirst and second emotional scoring functions are executed as paralleltasks and represented as parallel tasks in the logic of the message. 20.The method of claim 11, wherein the function comprises a first emotionalscoring function for a first emotion and another task includes a secondemotional scoring function for a second emotion, wherein both of thefirst and second emotional scoring functions are executed as paralleltasks and represented as parallel tasks in the logic of the message.